PREDICTIONS ON AND ANALYSIS OF VIRAL PROTEINS ENCODED BY OVERLAPPING GENES

Mahvash Khosravi

Submitted to the faculty of the Bioinformatics Graduate Program in partial fulfillment of the requirements for the degree Master of Science in the School of Informatics, Indiana University

August 2007

Accepted by the Faculty of Indiana University, in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics.

Master’s Thesis Committee ______A. Keith Dunker, PhD, Chair

______Pedro Romero, PhD

______Vladimir N. Uversky, PhD

ii

Dedicated to the memory of my mother

iii Acknowledgements I wish to express my gratitude to my thesis advisor Dr. A. Keith Dunker for his guidance, support and providing me the opportunity to work on this project. I would like to thank Dr. Pedro Romero for his advice and valuable input through the course of this research, as well as serving on my thesis committee. His guidance and encouragement is greatly appreciated. Also, I would like to thank Dr. Vladimir Uversky for serving on my thesis committee.

Finally I would like to express my gratitude and love for my family. I am indebted to my husband Dr. Hossein Hariri for his endless love, dedication, patience and encouragement. Thank you Hossein.

My deepest appreciation and love go to our beautiful daughters, our sweet engineer Ghazal and dear Homa who makes us so proud.

iv Abstract

Overlapping genes are adjacent genes that share a portion of their coding

sequence. Such genes are often observed in the compact genomes of viruses, prokaryotes,

and mitochondria. Overlapping genes are also seen in human and other mammalian

genomes. Gene overlapping is a phenomenon to minimize genomic size and maximize encoding capacity. Overlapping genes produce different proteins. A major task in the post genomic era is the large-scale study of the structures and functions of proteins. Proteins play crucial roles in virtually all biological processes. In general it is assumed that 3-D structure determines the function of proteins, but many proteins or region of proteins may function in the absence of 3-D structure. The term “disordered” is used to describe these proteins. A large number of studies has shown that biological functions depend on both ordered and disordered proteins. Natively disordered regions are common and play

essential roles in many proteins, especially, with regard to activities involved in signaling

and regulation.

The goal of this research was the analysis of the ordered and disordered

tendencies of viral proteins encoded by overlapping genes. Our hypothesis is that, in a

pair of proteins or protein regions encoded by overlapping genes, at least one of the pair

is disordered (or unstructured). Our hypothesis is based on the observation that structural

proteins require highly specific amino acid sequences, while unstructured (disordered)

sequences are essentially unconstrained. Thus, given a structural protein and its

associated mRNA sequence, any sequence derived from an overlapping reading frame

seems highly unlikely to have a sequence pattern commensurate with a structural protein;

on the other hand, a sequence pattern consistent with a disordered protein seems much

v more likely. We performed studies on the protein products of overlapping gene sequences, tested the hypothesis and addressed the following two questions: First do the proteins encoded by overlapping genes have opposite order-disorder content, that is, does the ordered part of one of the overlapping proteins correspond to a disordered part in the other overlapping protein? Second, does the encoded protein in the overlapping regions have more disordered amino acids than the non-overlapping regions?

Using our database of overlapping viral genes and the protein predictor PONDR

VL3, we predicted the order-disorder of amino acids in the sequence of 97 viral protein samples. An analysis of the results supported our hypothesis and indicated that the ordered amino acids are mostly associated with non-overlapping regions while disordered amino acids are more prevalent in overlapping regions. In the overlapping regions for 52 protein pairs, we showed that most of the amino acid pairs facing each other on the protein sequences had at least one disorder for most cases. Out of 52 pairs, there were 3 protein pairs where there were no disordered amino acids and 22 protein pairs where there were no ordered amino acids on either sequence. The fraction of ordered pairs in the pool of overlapping regions of 52 protein pairs was 0.28. The non-overlapping region of

97 proteins had predominantly ordered proteins. The fraction of ordered amino acids in the pool of non-overlapping regions was determined to be 0.77.

vi Table of Contents

List of Tables …………………………………………………………………………...viii List of Tables ………………………………………………………………………….....ix I. Introduction………………………………………………………………………. 1 II. Background………………………………………………………………………. 2 Overlapping genes…………………………………………………………… 2 Protein structure-function paradigm………………………………………… 4 Protein folding……………………………………………………………… 6 Secondary structure of protein………………………………………………. 8 Beta bends………………………………………………………………….. 11 Tertiary structure of protein………………………………………………… 11 Natively disordered proteins – the new paradigm………………………….. 12 Characterization of natively disordered proteins…………………………… 14 NMR spectroscopy…………………………………………………………. 14 X-ray crystallography………………………………………………………. 14 Circular dichroism (CD) spectroscopy………………………………………15 Protease sensitivity…………………………………………………………. 15 Frequency of natively disordered proteins…………………………………. 16 Function of disordered proteins…………………………………………….. 17 Molecular recognition……………………………………………………… 18 Protein modification……………………………………………………….. 18 Entropic chain activities…………………………………………………… 18 Amino acid compositions of natively disordered proteins………………… 18 Prediction of natively disordered proteins………………………………… 19 Objective……………………………………………………………………. 20 III. Materials and Methods…………………………………………………………. 22 Software……………………………………………………………………. 22 Data analysis………………………………………………………………. 23 Data management…………………………………………………………. 24 Database design, construction and implementation……………………….. 32 VIRUS data schema……………………………………………………….. 33 VIRUS database construction……………………………………………… 37 Database implementation………………………………………………….. 39 Populating and query the database………………………………………… 40 Database queries…………………………………………………………… 40 Prediction of proteins encoded by overlapping genes…………………….. 43 Extraction of proteins encoded by overlapping genes……………………. 44 IV. Results…………………………………………………………………………. 46 Overlapping regions of protein pairs……………………………………… 46 Non-overlapping regions of proteins……………………………………… 50 Bootsrapping……………………………………………………………….. 51 V. Discussion……………………………………………………………………… 53 VI. Conclusions……………………………………………………………………. 58 VII. Recommendations for future work……………………………………………. 60 VIII. References …………………………………………………………………….. 61

vii List of Tables Table 1. List of software used for this research project………………………………… 23

Table 2. Dataset of Viral Proteins………………………………………………………. 24

Table 3. Description of Information on Viruses in Our Dataset…………………………32

viii List of Figures

Figure 1. A Polypeptide chain with four amino acid residues…………………………… 6

Figure 2. Rigid CO-NH bond and rotation of Cα-C and Cα-N bonds……………………. 7

Figure 3. Ramachandran plot for 1000 nonglycine residues in eight proteins ……...... 8

Figure 4. Alpha helix…………………………………………………………………….. 9

Figure 5. Beta sheet………………………………………………………………………10

Figure 6. Beta bend…………………………………………………………………….. 11

Figure 7. Three dimensional protein structural motif …...... 12

Figure 8. 3-D structure of Calcineurin…………………………………………………. 17

Figure 9. Schema for VIRUS database…………………………………………………. 35

Figure 10. Physical Schema Of DNA_VIRUS Database………………………………. 39

Figure 11. Examples of queries…………………………………………………………. 41

Figure 12. Example of fasta format of protein sequences………………………………. 43

Figure 13. A sample output……………………………………………………………… 45

Figure 14. Results of protein prediction in Excel Spreadsheet…………………………. 48

Figure 15. Percentages of order and disorder calculated by Excel Spreadsheet……….. 48

Figure 16. Analysis of Order-Disorder for Overlap Proteins ………………………….. 49

Figure 17. Analysis of Overlap Proteins with Disorder Breakdown…………………… 50

Figure 18. Results of protein prediction for non-overlap region ………………………. 51

Figure 19. Fraction of Ordered Amino Acids in the Non-overlap Region of Proteins… 52

Figure 20. Overlapping protein pairs with more than 50% disorder……………………. 55

Figure 21. Fraction of Ordered Amino Acids in the Non-overlap Region of Proteins… 56

Figure 22. Fraction of Disordered Amino Acids in the Entire Sequence of Proteins…. 57

ix I. Introduction

Overlapping genes are adjacent genes that share a portion of their coding sequence. They are often observed in compact genome of viruses, prokaryotes, and mitochondria. Overlapping genes also occur in mammalian genomes.

Overlapping genes encode different protein products using the same nucleic acid sequence. Proteins play crucial roles in virtually all biological processes. For many years it was thought that 3-D structure of proteins determine their functions [1,2,3]. But there are proteins or region of proteins which lack 3-D structure, yet such proteins and regions function in the absence of any specific fixed structure [4,5]. These proteins, called natively disordered proteins, have many important roles in biological processes, specifically in cell cycle control, signaling and regulation [6]. Thus, detailed study and understanding of structure and function of natively disordered proteins is important and may eventually lead to finding cure for human diseases and novel medical products.

To the best of our knowledge, what is presented in this thesis is the first attempt to study disordered proteins expressed by overlapping genes. In this work the focus is on the protein products of viral overlapping gene sequences. First do the proteins encoded by overlapping genes have opposite order-disorder content, that is, does the ordered part of one of the overlapping proteins correspond to a disordered part in the other overlapping protein? Second do the overlapping regions of these proteins have a higher percentage of disordered amino acids than the non-overlapping regions?

In this introduction, we will first discuss the overlapping genes, proteins expressed by these genes, and the protein structure function paradigm. We then discuss

1 natively disordered proteins, how they are predicted, their frequency and function, their amino acid composition and their significance.

II. Background

Overlapping genes

A gene is any given segment along the DNA that encodes instructions that allow a

cell to produce a specific product, typically, a protein such as an enzyme that initiates a specific action. There are between 50,000 and 100,000 genes, and every gene is made up of thousands, sometimes even hundreds of thousands, of chemical bases [7].

Individual genes can overlap and share portion of their nucleotide sequence.

Overlapping genes are defined as adjacent genes which share a portion of their nucleotide sequence [8,9]. Overlapping genes might have occurred by an overprinting mechanism or by rearrangements [10,11]. The overprinting mechanism is the process of generating new genes from pre-existing nucleotide sequences utilizing a frame shift phenomenon. This phenomenon allows overlapping genes to encode different protein products using the

same nucleic acid sequence. Rearrangement is a process where the loss of a stop codon in

a specific gene causes the gene to elongate to the stop codon of the next gene.

Comparative analysis of the genomes of Mycoplasma genitalium and Mycoplasma

pneumoniae has revealed a rearrangement process in which the overlapping genes are generated by mutations at the ends of coding regions [12]. The rearrangement process

may occur in two ways; first is when the 3’-untranslated region and polyadenylation

signal of a gene is lost, but somehow it may utilize the 3’-untranslated region and

polyadenylation signal on the opposite strand of a neighboring gene. Second is when two

genes somehow become neighbor and initially do not overlap, later one of the genes loses

2 its polyadenylation signal but ends up utilizing the polyadenylation signal that is present on the non-coding strand of the other gene [14].

An example of gene overlapping is described below. This example shows the frame shift phenomenon where changes in the reading frame of a nucleotide sequence leads to the production of different amino acid sequences.

GAACCCAAAAAGAAACGAAAATCTGTTCGCTTG

Leu Gly Phe Phe Phe Ala Phe Arg Gln Ala Asn GAACCCAAAAAGAAACGAAAATCTGTTCGCTTG

Liu Gly Phe Ser Leu Leu Leu Asp Lys Arg GAACCCAAAAAGAAACGAAAATCTGTTCGCTTG

Trp Val Phe Leu Cys Phe Stop Thr Ser Glu

The overlapping gene phenomenon has been suggested to be beneficial for the viruses and other organisms for several possible reasons. Overlapping genes can reduce the size of the genome without affecting the number of genes encoded. Overlapping genes can produce new proteins without increasing the size of genome. Overlapping genes can coordinate the expression level of functionally related genes. Overlapping genes can coordinate the expression of genes where the expression of one gene requires the deactivation of the other [11,12]. Gene overlapping is normally observed in compact genomes that have high rates of mutation such as viruses, bacteria and organelles like mitochondria [15]. Overlapping genes are also relatively frequent in human and other mammalian genome [16].

The origin and evolution of overlapping genes have been the subject of a number of studies which suggest that these genes are produced by evolutionary mechanisms to

3 decrease the genome size while increasing the number of genes [12,13,17,18,19,20]. It is speculated that the rates of evolution are slower in overlapping genes [21]. Recently, it

has been suggested that evolution of overlapping genes occurs at a universal mutation rate

across bacterial genomes [13]. More studies are needed to learn about the origin,

evolution and cross-species conservation, and frequency and genome-wide distribution of overlapping genes in different genomes.

Overlapping genes may offer information about how coding and control sequences have evolved. It can also provide information about evolution patterns among classes of organisms [22]. Studies and comparison of overlapping genes in related species may help us understand how and under what conditions overlap evolved [13]. Gene overlap has been associated with a number of human disease genes since genomic rearrangements are likely to occur within overlapping regions possibly because of inconsistent sequence features common in these regions [23].

Protein structure-function paradigm

Proteins play crucial roles in virtually all biological processes. Proteins can act as enzymatic catalysts as well as assist in storage, coordination, transportation, and motion;

provide mechanical support and immune protection; and aid in growth control,

differentiation, nerve impulse transmission, and nerve impulse generation. Proteins, as a

distinctive characteristic, have well-defined 3-D structures. According to the structure-

function paradigm, the amino acid sequence specifies a protein’s 3-D structure and the

3-D structure must be present for the protein to function. Fischer’s “lock and key”

proposal in 1894 [24] was an early concept that eventually led to the structure-function

paradigm. Fischer used his studies on enzymes which hydrolyzed different bonds to draw

4 conclusions that led to his proposal. Studies by Wu in 1930’s showed that the addition of

heat or solutes to globular proteins causes their denaturation and loss of biological activity [25]. Pauling and Mirsky also reached the same conclusion as Wu [1]. Many years later, Anfinsen made the critical observation that ribonuclease denaturation is

reversible and showed that the information needed to specify the 3-D structure of

ribonuclease is contained in its amino acid sequence [26]. Thus, amino acid sequence is

important because it specifies the conformation of protein. X-ray crystallography studies

of structures of myoglobin and hemoglobin have also confirmed the structure-function

paradigm [27]. In contrast to the focus on protein structure, Williams in his nuclear

magnetic resonance spectroscopy revealed that some proteins lack defined and folded

structures in solution [28].

Amino acids are the basic structural units of proteins which are linked by peptide bond and form a polypeptide chain. Each protein consists of one or more unique polypeptide chains. The amino acid sequence of a polypeptide chain forms the primary structure. Different regions of the amino acid sequence form local regular secondary structure, such as alpha helices or beta strands. Association of alpha helix and beta strands leads to folding of the protein.

Structural domains, which are compact globular units, are formed by interaction between elements of secondary structures and their side chains. The tertiary structure is the totally folded polypeptide chain and it may include one or more domains. Fully folded polypeptide chains may interact with other polypeptide chains and form a larger structure called subunit. The overall assembly is referred to as quaternary structure. By formation of such tertiary and quaternary structures, amino acids far apart in the sequence

5 are brought close together in three dimensions and form a functional region, the active

site. Proteins must recognize thousands of different molecules in the cell by detailed

three-dimensional interactions, which require diverse and irregular structures of the

protein molecules, the most important of which is their secondary structure [29].

Protein folding

As mentioned earlier, the amino acid sequence of a polypeptide chain forms the

primary structure of a protein. In general, the information contained in the primary structure determines the manner by which protein folds. There are other forces that play roles in protein folding as will be discussed in this section.

There are twenty amino acids. A combination of amino acids makes a polypeptide chain from which proteins are formed. Each amino acid consists of an amino group, a carboxyl group, a hydrogen atom and a variable side chain (R group), which are bonded to a carbon atom called α-carbon. Each side chain differs in shape, size, charge, hydrogen bonding capability and chemical reactivity. Amino acids are linked by peptide bonds to form a polypeptide chain as shown in Figure 1. An amino acid unit in a polypeptide is called a residue.

Figure 1. A Polypeptide chain with four amino acid residues H H H H

+H3N C CO NH C CO NH C CO NH C COO

R R R R

In the late 1930s, Linus Pauling and Robert Corey [30] carried out x-ray crystallographic studies on peptides and found that in a polypeptide chain, the CO-NH

6 peptide unit is rigid and planar. In contrast, the bonds between α-carbon and NH and CO groups are single bonds which give them a large degree of rotational freedom. Rotation about the Cα-N bond is labeled φ and the one about Cα-C bond is labeled ψ as shown in

Figure 2.

Figure 2. Rigid CO-NH bond and rotation of Cα-C and Cα-N bonds

H H H H φ φ φ ψ

+H3N C CO NH C CO NH C CO NH C COO ψ ψ ψ ψ R R R R

Positive variation in φ corresponds to a clockwise rotation when viewed from Cα

toward N. For ψ positive variation corresponds to a clockwise rotation when viewed from

Cα toward C. The conformation corresponding to φ = ψ = 0 is when two CO-NH planes connected to a common Cα lie in the same plane. In principle φ and ψ can have any value between -180 to +180 degrees, however, many φ, ψ angular combinations are impossible because of steric collisions between atoms along the backbone or between backbone atoms and the side chain R group [31]. The polypeptide conformation has been represented by points on a ψ versus φ plot called Ramachandran plot. Figure 3 shows a

Ramachandran plot for 1000 nonglycine residues in eight proteins [31].

7

Figure 3. Ramachandran

plot for 1000 nonglycine

residues in eight proteins

Since protein folding takes place in an aqueous environment, the interaction between polypeptide and water plays an important role in protein folding. The folding of water-soluble globular protein is due to minimizing the extent of exposure of hydrophobic group to the solvent. As a result the side chains are packed into the interior of the molecule which leads to a hydrophobic interior and a hydrophilic surface. The main chain folds into the interior with the side chains. The main polypeptide chain is hydrophilic because it is highly polar. In each peptide unit the NH group is hydrogen bond donor and the CO group is hydrogen bond acceptor. In a hydrophobic environment these polar groups are neutralized by hydrogen bond formation. This is facilitated by the formation of regular secondary structure within the interior of the protein molecule.

Finally, hydrogen bonding between elements of the peptide backbone leads to the formation of secondary structure.

Secondary structure of protein

In 1951 Pauling and Corey [30] proposed two models for the secondary structure of protein, alpha helix and beta pleated sheet. The alpha helix is a rodlike structure, as

8 shown in Figure 4, with a tightly coiled polypeptide forming the inner part of the rod and the side chains extend outward in a helical array.

9 Figure 4. Alpha helix

Cα

Cα O H O C N C Cα N Cα C N Cα H O Cα H

Cα

Cα O H O C N C Cα Cα N Cα C Cα N H O H Cα

Cα

The most common secondary structure in currently known globular proteins is the alpha helix. The Alpha helix is stabilized by hydrogen bonds between the NH and CO groups of the main chain. All the hydrogen bonds in an alpha helix point in the same direction so the peptide units are aligned in the same orientation along a helical axis.

There is a partial positive charge at the amino end and a partial negative charge at the carboxyl end of an alpha helix. This produces a significant net dipole for the alpha helix.

These charges attract ligands of opposite charge. Alpha helix in a protein is basically the outside of the protein with one side of the helix facing the solution and the other side toward the hydrophobic interior of the protein.

Beta sheet is the second most common type of structural element found in currently known globular proteins. The beta sheet differs from alpha helix, in that it looks like a sheet and it is almost fully extended rather than being tightly coiled as in alpha helix. Beta pleated sheets are formed when two or more polypeptide chains are brought

10 together side by side. In this case the NH group of an amino acid residue on one chain forms a hydrogen bond with the CO group of the adjacent chain.

The strands of beta-sheets can run in one direction in a parallel arrangement. In an anti-parallel arrangement, sheets run in opposite directions. In a mixed-sheet arrangement some strands are parallel and others are anti-parallel as shown in Figure 5.

Figure 5. Beta sheet

11 Beta bends

In the previous section we discussed alpha helix and beta sheets as two forms of secondary structure of polypeptide chains. In order to fold this chain to a compact globular form, the polypeptide chains must be able to change direction. A commonly observed way to facilitate this change in direction is a beta bend. As shown in Figure 6, beta bend is a tight loop in which a CO group forms a hydrogen bond with the NH group of the residue three positions farther along in the polypeptide chain.

O C α4 α3 N C O H N H Figure 6. Beta bend O α2 C α1 N H

Tertiary structure of protein

Efficient packing of secondary structural elements is another important feature that leads to the tertiary structure of a protein. Alpha helices and beta sheets are packed together to form subunits or domains that are functional units of tertiary structure of a protein. In globular proteins, it has been frequently observed that adjacent alpha helices and beta sheets pack together and connected by loop regions to form a three dimensional protein structural motif as shown in Figure 7. The structural motifs are normally formed such that

12

Figure 7. Three dimensional protein structural motif

they have a minimum accessible surface area. In some cases alpha helices may form complex and irregular geometries than structural motifs mentioned earlier. However, even in these cases it seems that the geometric restrictions that lead to close packing are still present.

Domains are classified into different main structural groups including alpha, beta and alpha/beta structures. In alpha structure, the core is built up exclusively from alpha helices. Beta structures comprise antiparallel beta sheets. Alpha/beta structures, consists of a combination of beta-alpha-beta motifs. The combination of domains in a single protein determines its overall function [29]. A protein may contain one or more structural domains. The domains of large proteins are usually connected by relatively flexible regions of polypeptide chains [31].

13 Natively disordered proteins - the new paradigm

Evidence is growing that dominant view of structure-function paradigm does not

hold universally for all proteins. In contrast many proteins or region of proteins may

function in the absence of 3-D structure. These proteins may lack specific 3-D structure and may be partially or completely unfolded in their native state. The terms natively denatured, intrinsically unstructured, or disordered proteins are used to describe these proteins. Natively disordered proteins or regions of protein usually have dynamic Φ and

Ψ angles. Natively disordered proteins are characterized by X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD) spectroscopy and protease sensitivity among many others.

Two ordered proteins with identical sequences would basically have the same structure. But two disordered proteins with the same sequence may each have a different conformation which may vary for each protein over time [32]. This is because molecules that make up natively disordered proteins do not reside in a fixed position in space relative to each other, but instead occupy different positions relative to each other over

time and across different proteins with the same sequence.

Natively disordered proteins are found in majority of species. Based on structure

and function of these proteins, Tompa [33] has proposed to classify them as a separate

entity. In general natively disordered proteins can be divided into two major groups:

extended and collapsed [34]. The extended disorder refers to unfolded protein and

regions that exist as random coil. While extended disordered proteins may have

secondary structure, but due to fluctuation of Ramachandaran angles in the backbone, this

structure is transient. The collapsed disorder refers to proteins and domains that resemble

14 molten globules which may have partially folded secondary structure with a dynamic

tertiary structure [35]. Therefore, each protein may have three possible states: order,

extended disorder, and collapsed disorder. Protein trinity refers to these three possibilities

[36]. Proteins may change shape and take a form appropriate to any of the three states

mentioned above depending on their environment.

Characterization of natively disordered proteins

Methods that are used to identify and characterize natively disordered proteins

include NMR spectroscopy, X-ray crystallography, circular dichroism spectroscopy and

protease sensitivity.

NMR spectroscopy

Nuclear magnetic resonance spectroscopy is a specific method that is used to

identify structure and dynamic of natively disordered proteins. NMR can detect molecules which are moving rapidly. Natively disordered proteins have dynamic structures, i.e., they can convert between different states depending on the events through which they undergo. Thus, NMR can identify regions that are disordered as well as any transient secondary or tertiary structure that is present. NMR spectroscopy is associated with technical difficulties when it performs on molten globular proteins. Therefore, this technique is mostly used to detect extended disorder [4].

X-ray crystallography

Proteins that are ordered form crystals, but disordered proteins do not. When both ordered and disordered regions of a protein are subject to X-ray crystallography, the disordered proteins do not scatter x-rays the same way as ordered region do. This leads to missing electron density in the final structure [32]. As a result, completely disordered

15 proteins can not be studied by X-ray crystallography method. Also, disordered proteins

are unlikely to form crystals in the first place. However, proteins consisting of both

ordered and disordered regions can be studied because the ordered regions scatter x-rays,

and the disordered regions occupy spaces between the ordered parts.

Circular dichroism (CD) spectroscopy

Proteins with tertiary structure can be detected by intense near-UV CD spectra

while natively disordered proteins are characterized by low intensity near-UV CD spectra of low complexity. Far-UV CD spectrum is able to provide information about secondary structure of proteins. In circular dichroism spectroscopy, a combination of near- and far-

UV CD is used to differentiate the ordered and disordered proteins (that is extended disorder and collapsed disorder). Circular dichroism spectroscopy can only provide information about presence of natively disordered regions but not about their locations within the sequence [4].

Protease sensitivity

Protease enzymes can be used to study natively disordered proteins. These enzymes digest specific sites of the unfolded protein sequence, thus, disordered proteins are digested rapidly. The rate of digestion of fully unfolded proteins can be on the order of 103 times faster than ordered proteins.

Due to the limitations associated with each of the method mentioned above, study

of natively disordered proteins may require application of more than one method.

In biological systems disordered proteins do not degrade by proteases because

they form a complex with binding partner and they are at least partially folded or in some

cases are located in protease deficient regions of the cells.

16 A disordered protein example

Calcineurin is involved in many biological responses including lymphocyte

activation, neuronal and muscle development. Calcineurin is a major protein of the brain.

It is a calcium calmodulin-dependent serine/threonine protein phosphatase. Calcineurin is

composed of two catalytic A and B subunits. The A subunit contains the catalytic

elements [37], a calmodulin binding domain [38], and autoinhibitory elements [39]. The

B subunit is Ca2+-binding protein which remains tightly associated with the A subunit.

Calcium-calmodulin complex binds to the target helix within calcineurin, then autoinhibitory peptide disassociates from the active site and causes the phosphatase activity to turn on. Studies show that calcium-calmodulin complex wrap around the target helix, therefore, this region must lack tertiary structure and lie within the disordered regions.[40,41]

Figure 8 shows a 3-D structure of Calcineurin where A subunit is shown in yellow, B subunit is shown in blue, the autoinhibitory peptide in green, the location of

95-residue disorder region in red and camodulin binding site as red helix.

Frequency of natively disordered proteins

Studies show that on the average more than 30% of eukaryotic proteins and 4.2% of bacterial proteins are either completely or partially unfolded. Analysis of genomic sequence of different organisms shows that the proportion of sequence that code for natively disordered proteins depends on the complexity of the organism. Thus, disordered proteins are common in eukaryotes and not very common in bacteria [42].

17 Figure 8. 3-D structure of Calcineurin

Functions of disordered proteins

Although natively disordered proteins or regions of proteins lack specific order but they may have local and limited residual structure that allows them to interact and bind with different proteins, nucleic acids and membranes [43,44]. Studies show that disordered region might be of advantage to a protein because it allows efficient interaction with different regions of a single or a multiple target [45].

In a study of 115 disordered regions [5], twenty eight functions associated with 98 of these regions were identified. These functions were classified into four main groups as follows: molecular recognition, assembly/disassembly, protein modification and entropic chain activities

18 Molecular recognition

Many cellular activities such as gene expression and signal processing are

dependent on dynamic and efficient macromolecular interactions which are facilitated by

disordered regions. Protein binding, nucleic acid binding, and receptor-ligand binding are examples of these molecular interactions.

Assembly/disassembly is basically the same as molecular recognition and may be considered as its special case.

Protein modification

Protein modification, such as, phosphorylation, glycosylation, and methylation,

occurs in the disordered regions. A recent study shows that chemical modification is also

frequent in both RNA and protein chaperones [46].

Entropic chain activities

A collection of protein functions that depend on disordered regions without any induction of order is called “entropic chain activities”. This group of functions depends

on the flexibility or rigidity of the disordered region.

Amino acid compositions of natively disordered proteins

Although functions of many proteins are determined by their 3-D structure,

disordered proteins or regions possess biological functions, too. The sequences of

natively disordered regions are evolutionary conserved and mainly consist of amino acids

of low hydrophobicity with large net charge. The disordered regions may also have

sequences of low complexity and high flexibility. [47]. The amino acid compositions of

disordered proteins have a higher level of specific amino acids such as E,K,R,G,Q,S and

P. They also have a lower level of the amino acids I,L,V,W,F,Y,C and N [47,48]. Based

19 on these specific amino acid composition disordered regions of proteins can be predicted.

There are several programs that can identify these disordered regions.

Prediction of natively disordered proteins

Predictors of Natural Disordered Regions (PONDRs) is a neural network predictor that uses amino acid sequence data to predict disorder in a given region [48,49].

Basically PONDRs use sequence attributes taken over windows of 9 to 21 amino acids.

The values used to train the neural network are average of attributes such as fractional compositions of the specific amino acids and hydropathy taken over these sequence

windows around the residues of interest.

The earliest predictors of disordered proteins were the VL1 predictor [48], the N-

and C- terminal predictors (XT) [50]. These predictors used feed forward neural networks

to predict natively disorder proteins and had an relatively good accuracy (about 73%)

against testing data [51]. Later on the PONDR VL-XT, which is a combination of the

earliest versions, was developed. PONDR VL-XT uses neural networks to predict order– disorder class for every amino acid residue in a protein. The extensions added to PONDR

describes the training data of each specific predictor and explained elsewhere [52].

The PONDR VL-XT was trained against long regions (40 or more residues) of disorder identified from regions missing in x-ray structures [48,50]. Additional predictors were developed using different neural networks as well as logistic regression. All of these predictors calculate values for different attributes of each amino acid residue and feed them into either a neural network or a linear predictor. The attributes used to predict the disorder amino acid residues are the frequency of certain amino acids or types of amino acids, hydropathy, and coordination number. Each attribute is calculated as the

20 normalized value of the feature over a sliding window [48]. PONDR VL-XT outputs for

each residue are numeric value between 0 and 1. One is the ideal prediction for disorder

and 0 is the ideal prediction for order. Usually PONDR VL-XT does not output these

numbers, thus, a threshold of 0.5 is applied. Amino acid residues with values of greater or

equal to 0.5 are assigned. Later on CDF (Cumulative Distribution Function) analysis

from VL-XT predictor was developed to predict proteins which are completely

disordered (wholly ordered). CDF summarizes the frequency of disorder scores from

PONDR VL-XT. Based on a distribution of prediction scores it then classifies the protein

as ordered or disordered [53].

The VL-XT predictor shows a higher accuracy to study short regions of either order or disorder. PONDR VL3 predictor that was used in this study was designed using longer sequence windows and showed a better prediction accuracy of order and disorder regions. PONDR VL3 has a high rate of accuracy of 85% and is based on averaging the

outputs of an ensemble of 50 predictors. Therefore, it tends to predict disorder with less

granularity [34].

Objective

As mentioned earlier, we would like to focus on the protein products of viral

overlapping gene sequences in order to test our hypothesis that a pair of proteins encoded

by overlapping genes have opposite order-disorder content. This means that an ordered

amino acid on one sequence corresponds to a disordered amino acid on the other

sequence. Moreover, the overlapping region of these proteins have a higher percentage of

disordered amino acids than the non-overlapping region. In this study we will use 97

21 proteins (52 pairs) that are encoded by overlapping genes. We would like to predict the proportion of disordered proteins using PONDR VL3 predictor.

22 III. Materials and Methods

The available data for this research were in the form of a Microsoft Excel table.

This table was provided to us by our collaborators from Architecture et Fonction des

Macromolecules Biologiques (AFMB) at Ecole de l’AND in Marseille, France, who are studying viral proteins encoded by overlapping genes. The rows of the table were attributed to different viruses of interest. The numerous columns of the table provided information on different characteristics of each virus. We used the available data to investigate our hypothesis mentioned earlier. We accessed the computer hardware facilities as well as personal computers at the Center for Computational Biology and

Bioinformatics at IUPUI in Indianapolis and a number of software to carry out this research.

Software

The software used for this research project is listed in Table 1. We also used

Excel spreadsheet capabilities as needed. MySQL was used to create a relational database to store and query data.

MySQL is an open source relational database management system. It uses Structured

Query Language (SQL), which is the most popular language for adding, accessing, searching and processing data in a database. Data definitions in SQL were used to create and alter the descriptions of the tables (or relations) of the database. SQL systems were used to specify primary keys and referential integrity constraints. Basic SQL queries like the select, delete, insert or alter statements were used for inserting information into and retrieving information from the database.

23 Table 1. List of software used for this research project

Name Suppliers Usage License Terms MySQL http://www.mysql.com Relational GNU General Public v4.1.14 database License ® PONDR http://www.pondr.com Prediction of Individually licensed VL3 order/disorder from Molecular for protein Kinetics sequences XEmacs http://www.xemacs.org Text editor GNU General Public v21.3.1 License Perl v5.8.0 http://www.activestate.com Perl interpreter GNU General Public License EditPlus http://www.editplus.com Text editor Individually v2.12 licensed from Dawn Roberts

® PONDR VL3 was the software that we used to calculate the residual values for

each amino acid in the protein sequence. We could use these residual values to predict

order and disorder of the protein sequence.

We used XEmacs to prepare perl scripts that were needed to carry out some of the tasks and calculations for the project.

Perl v5.8.0 was used as interpreter to run the perl scripts.

Data analysis

Analysis of data required the following steps:

1. Data management

2. Database design, construction and implementation

3. Populating and query the database

4. Prediction of order/disorder of amino acid sequence encoded by overlapping

genes using PONDRs

24 5. Extraction of the information related to the order/disorder of amino acid

sequence.

Data management

As mentioned earlier data on viral proteins encoded by overlapping genes were

provided to us our collaborators in France. The data included single and double stranded

RNA viruses as well as a number of circular and linear DNA viruses as shown in Table 2.

Table 2. Dataset of Viral Proteins

Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

Viruses; dsRNA viruses; Infectious ds-RNA 1/ VP5 2/ polyprotein 1 NC_001915 Birnaviridae; pancreatic & linear (modified, second Aquabirnavirus necrosis virus ATG is used) Viruses; dsRNA viruses; Infectious ds-RNA 1/ VP5 protein 2/ VP2-4-3 2 NC_004178 Birnaviridae; bursal disease & linear polyprotein Avibirnavirus virus Viruses; dsRNA viruses; Mammalian ds-RNA 1/ minor capsid 2/ nonstructural Reoviridae; orthoreovirus 1 & linear cell attachment protein sigma- 3 NC_004267 Orthoreovirus; protein sigma-1a ibNS Mammalian orthoreoviruses Viruses; dsRNA viruses; Rice ragged ds-RNA 1/ RNA- 2/ P4b Reoviridae; Oryzavirus stunt virus & linear dependent RNA NC_003771 4 polymerase

Viruses; dsRNA viruses; Rice dwarf ds-RNA 1/ nonstructural 2/ OP-ORF 5 NC_003768 Reoviridae; virus & linear protein (new) Phytoreovirus Viruses; dsRNA viruses; Saccharomyces ds-RNA 1/ capsid 2/ RNA Totiviridae; Totivirus cerevisiae virus & linear polymerase 6 NC_001641 L-BC (La)

Viruses; ssRNA Bunyamwera ss-RNA 1/ N protein 2/ NSs negative-strand viruses; virus & linear protein 7 NC_001927 Bunyaviridae; Orthobunyavirus

25 Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

Viruses; ssRNA negative- Measles virus ss-RNA & 1/ 2/ nonstructural strand viruses; linear phosphoprotein C protein Mononegavirales; 8 NC_001498 Paramyxoviridae; Paramyxovirinae; Morbillivirus

Viruses; ssRNA negative- Tupaia ss-RNA 2/ 3/ nonstructural strand viruses; paramyxovirus & linear phosphoprotein protein V 9 NC_002199 Mononegavirales; Paramyxoviridae; Paramyxovirinae

2/ 4/ nonstructural 10 ” ” ” ” phosphoprotein protein C Viruses; ssRNA negative- Mossman virus ss-RNA 2/ 3/ V protein strand viruses; & linear phosphoprotein 11 NC_005339 Mononegavirales; Paramyxoviridae; Paramyxovirinae

2/ 4/ C protein 12 ” ” ” ” phosphoprotein Viruses; ssRNA negative- Sendai virus ss-RNA 2/ C' protein 3/ P protein (co- strand viruses; & linear factor of RNA Mononegavirales; polymerase) 13 NC_001552 Paramyxoviridae; Paramyxovirinae; Respirovirus

3/ P protein (co- 4/ V protein 14 ” ” ” ” factor of RNA (new) polymerase) [3/4] Viruses; ssRNA negative- Mumps virus ss-RNA 2/ phoshoprotein 3/ V protein strand viruses; & linear Mononegavirales; 15 NC_002200 Paramyxoviridae; Paramyxovirinae; Rubulavirus

Viruses; ssRNA negative- Vesicular ss-RNA 2/ NS protein 3/ Cprim strand viruses; stomatitis & linear protein (new) 16 NC_001560 Mononegavirales; Indiana virus Rhabdoviridae; Vesiculovirus

26 Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

Viruses; ssRNA positive- Lactate ss-RNA & 3/ structural 4/structural strand viruses, no DNA dehydrogenase- linear glycoprotein glycoprotein 17 NC_002534 stage; Nidovirales; elevating virus Arteriviridae; Arterivirus 4/ structural 5/ structural 18 ” ” ” ” glycoprotein glycoprotein Viruses; ssRNA positive- Mushroom ss-RNA & 1/ nd 2/ nd strand viruses, no DNA bacilliform linear 19 NC_001633 stage; Barnaviridae; virus Barnavirus 20 ” ” ” ” 2/ nd 3/ nd Viruses; ssRNA positive- Cucumber ss-RNA & 1/ RNA- 2/2b protein strand viruses, no DNA mosaic virus linear dependent RNA NC_002035 21 stage; Bromoviridae; polymerase Cucumovirus Viruses; ssRNA positive- Spinach latent ss-RNA & 2/ putative 2/ putative 2b strand viruses, no DNA virus linear polymerase protein 22 NC_003809 stage; Bromoviridae; Ilarvirus Viruses; ssRNA positive- Apple stem ss-RNA & 1/241k 2/36K protein strand viruses, no DNA grooving virus linear polyprotein 23 NC_001749 stage; Flexiviridae; Capillovirus Viruses; ssRNA positive- Blueberry ss-RNA & 5/ Coat protein 6/16 kDa strand viruses, no DNA scorch virus linear protein (putative NC_003499 24 stage; Flexiviridae; nucleic acid- Carlavirus binding protein) Viruses; ssRNA positive- Indian citrus ss-RNA & 5/ capsid protein 6/ putative 23 linear strand viruses, no DNA ringspot virus CP kDa nucleic acid NC_003093 25 stage; Flexiviridae; binding protein Mandarivirus Viruses; ssRNA positive- Bamboo ss-RNA & 1/ replicase 2/ hypothetical strand viruses, no DNA mosaic virus linear 14k protein 26 NC_001642 stage; Flexiviridae; Potexvirus Viruses; ssRNA positive- Cassava ss-RNA & 3/ triple gene 4/ triple gene strand viruses, no DNA common linear block protein 2 block protein 3 27 NC_001658 stage; Flexiviridae; mosaic virus Potexvirus

27 Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

Viruses; ssRNA positive- Apple chlorotic ss-RNA & 2/ movement 3/ coat protein strand viruses, no DNA leaf spot virus linear protein 28 NC_001409 stage; Flexiviridae; Trichovirus Viruses; ssRNA positive- Hepatitis E ss-RNA & 9/ nd 10/ nd strand viruses, no DNA virus linear 29 NC_001434 stage; Hepatitis E-like viruses Viruses; ssRNA positive- Barley stripe ss-RNA & 3/ beta C protein 4/ beta D 30 NC_003481 strand viruses, no DNA mosaic virus linear protein stage; Hordeivirus Viruses; ssRNA positive- Indian peanut ss-RNA & 4/ P14 protein 5/ P17 protein 31 NC_004730 strand viruses, no DNA clump virus linear stage; Pecluvirus Viruses; ssRNA positive- Potato mop-top ss-RNA & 2/ triple-gene- 3/ triple-gene- 32 NC_003725 strand viruses, no DNA virus linear block protein 2 block protein 3 stage; Pomovirus Viruses; ssRNA positive- Sesbania ss-RNA & 2/ polyprotein 4/ coat protein 33 NC_002568 strand viruses, no DNA mosaic virus linear stage; Sobemovirus Viruses; ssRNA positive- Tobacco bushy ss-RNA & 3/ unknown 4/ unknown 34 NC_004366 strand viruses, no DNA top virus linear stage; Umbravirus Viruses; ssRNA positive- Flock house ss-RNA & 1/ protein A 3/ protein B2 strand viruses, no DNA virus linear 35 NC_004146 stage; Nodaviridae; Alphanodavirus Viruses; ssRNA positive- Striped Jack ss-RNA & 1/ protein A 2/ protein B strand viruses, no DNA nervous linear 36 NC_003448 stage; Nodaviridae; necrosis virus Betanodavirus Viruses; ssRNA positive- Macrobrachium ss-RNA & 1/ RNA- 2/ B2 protein strand viruses, no DNA rosenbergii linear dependent RNA NC_005094 37 stage; Nodaviridae; nodavirus polymerase unclassified Nodaviridae Viruses; ssRNA positive- Theilovirus ss-RNA & 1/ viral 2/ viral protein strand viruses, no DNA linear polyprotein L (new) 38 NC_001366 stage; Picornaviridae; Cardiovirus

28 Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

Viruses; ssRNA positive- Nudaurelia RNA & 1/ RNA- 2/ capsid protein strand viruses, no DNA capensis beta linear dependent RNA NC_001990 39 stage; Tetraviridae; virus polymerase Betatetravirus Viruses; ssRNA positive- Dendrolimus ss-RNA & 1/ p17 2/ capsid protein strand viruses, no DNA punctatus linear p71 40 NC_005899 stage; Tetraviridae; tetravirus unclassified Tetraviridae Viruses; ssRNA positive- Pothos latent ss-RNA & 4/ hypothetical 5/ hypothetical strand viruses, no DNA virus linear protein, 27K protein, 14K 41 NC_000939 stage; Tombusviridae; Aureusvirus Viruses; ssRNA positive- Hibiscus ss-RNA & 1/ RNA- 3/ P28 protein strand viruses, no DNA chlorotic linear dependent RNA NC_003608 42 stage; Tombusviridae; ringspot virus polymerase Carmovirus 6/ coat protein 7/ hypothetical 43 ” ” ” ” protein Viruses; ssRNA positive- Maize chlorotic ss-RNA & 4/ p31 protein 6/ coat protein strand viruses, no DNA mottle virus linear 44 NC_003627 stage; Tombusviridae; Machlomovirus Viruses; ssRNA positive- Tobacco ss-RNA & 3/7 kDa protein 4/7 kDa protein strand viruses, no DNA necrosis virus linear 45 NC_003487 stage; Tombusviridae; D Necrovirus Viruses; ssRNA positive- Cymbidium ss-RNA & 4/ putative 5/ core protein strand viruses, no DNA ringspot virus linear movement protein p19 46 NC_003532 stage; Tombusviridae; Tombusvirus Viruses; ssRNA positive- Turnip yellow ss-RNA & 1/ overlapping 2/ strand viruses, no DNA mosaic virus linear protein/movement replicase/papain- NC_004063 47 stage; Tymoviridae; protein like protease Tymovirus Viruses; Retroid viruses; Cacao swollen DNA & 3/ polyprotein 5/ hypothetical 48 NC_001574 Caulimoviridae; shoot virus circular protein Badnavirus Viruses; Retroid viruses; Arctic ground DNA & 1/ e antigen 3/ polymerase Hepadnaviridae; squirrel circular precursor 49 NC_001719 Orthohepadnavirus hepatitis B virus

29 Overlapped Overlapped Acc Genome Taxonomy Organism CDS 1 / CDS 2 / number type Product 1 Product 2 Overlap

3/ polymerase 4/ large 50 ” ” ” ” envelope protein 51 ” ” ” ” 3/ polymerase 7/ X protein Viruses; Retroid viruses; Cestrum yellow DNA & 2/ putative virion 3/ putative 52 NC_004324 Caulimoviridae; leaf curling linear associated protein capsid protein Caulimovirus virus

Table 2 shows some information on 52 pairs. The data provided to us by our collaborators include other information in addition to what is provided in Table 2. The

descriptions of the information related to viruses are given in Table 3. A number of

viruses in Table 2 share the same name, identification number, taxonomy, genome and

overlap identification but different protein identification. In the database provided, in the

column “protein number” we see a(b) and b(a) in the rows related to the same virus

identification number. These rows refer to overlapping proteins.

30 Table 3. Description of Information on Viruses in Our Dataset

Virus Attribute Description Acc number Virus identification number (included in Table 2) Taxonomy Virus Taxonomy Organism Virus name (included in Table 2) Genome Virus genome (included in Table 2) Strain Virus strain GI Gene information identifier Tax_id NCBI taxonomy identifier genome length (bp) Size of virus genome Overlapping CDS Overlap identification (included in Table 2) begin(bp) overlap The base pair number where overlap begins end(bp) overlap The base pair number where overlap ends protein number Refers to overlap identification (included in Table 2) product Protein product (included in Table 2) sense of protein Refers to sense strand that expresses the protein prot_id of protein Protein identification GI of protein Gene information identifier lgth(bp) of protein Length of DNA that produces the protein lgth(aa) of protein Protein length seq aa of protein Amino acid sequence of protein begin(aa) of Beginning, or start position, of an overlapping protein(amino overlapping acid) sequence seq aa overlapping Overlapping protein (amino acid) sequence end(aa) of End position of an overlapping protein (amino acid) sequence overlapping length(aa) of Length of an overlapping protein (amino acids) overlapping

Database design, construction and implementation

A relational database is basically a collection of organized tables. The process of designing a database involves a number of steps. Based on the nature of our research, we needed to build a relational database to store and retrieve information about viral

31 proteins. To access and retrieve the information we had to organize the data in relevant

tables. First we identified the entities of our data model and decided that we needed four tables to organize the attributes of the entities as will be described later. Finally, we identified the relationship between the entities and the unique identifiers that facilitated the implementation of our data model.

VIRUS database schema:

Database schema describes the structure of tables and the relationship among them. The schema for VIRUS database consists of four tables as shown in Figure 9. This schema includes four tables, VIRUS_DES, CDS, OVERLAP and PONDRS. The table

VIRUS_DES (virus description) includes general information on the virus as follows:

! Class: Refers to virus genome

! virusid: Refers to virus identification number (Acc number)

! name: Refers to virus name

! taxo: Refers to virus taxonomy

! gi : Refers to gene information identifier

! taxid: Refers to the NCBI taxonomy identifier

! lgthgenome: Refers to the size of virus genome

Classification and taxonomy of the viruses have been established by the International

Committee on Taxonomy of Viruses (ICTV).

Table CDS includes information on genome sequence of the virus and the encoded

proteins as follows:

! cdsprotid: Refers to protein id

! cdsgi : Refers to coding sequence

32 ! overlapid: Refers to overlap id

! cdssense: Refers to gene sense strand

! cdscomplete: Refers to complete coding sequence

! cdsproduct: Refers to protein product

! cdsbegAN: Refers to the beginning or start position of gene (nucleotide) sequence

! cdsendAN: Refers to the end position of gene (nucleotide) sequence

! cdslgthAN: Refers to the length of gene (nucleotide) sequence

! cdslgthAA: Refers to the length of protein (amino acids)

! cdsfullseqAA: Refers to the sequence of protein.

! cdsfullseqAN: Refers to the sequence of the viral gene that expresses the above

protein .

33 Figure 9. Schema for VIRUS database

VIRUS_DES

Class virusid name taxo gi taxid lgthgenome

CDS

virusid cdsprotid cdsgi overlapid cdssense cdscomplete

cdsproduct cdsbegAN cdsendAN cdslgthAN cdslgthAA

cdsfullseqAA cdsfullseqAN

OVERLAP

overlapid cdsprotid overlapbegAA overlapendAA overlaplgthAA overlapseqAA

overlapbegAN overlapendAN overlaplgthAN

PONDRS

cdsprotid Prot_ position Pondr_VLXT Pondr_VL3 Pondr_VSL1

Table OVERLAP includes information on overlapping genes and their encoded

proteins as follows:

! overlapbegAA: Refers to the beginning, or start position, of an overlapping

protein (amino acid) sequence

34 ! overlaplgthAA : Refers to the length of an overlapping protein (amino acids)

! overlapseqAA : Refers to an overlapping protein (amino acid) sequence

! overlapbegAN : Refers to the beginning or start position of an overlapping gene

(nucleotide) sequence

! overlapendAN : Refers to the end position of an overlapping gene (nucleotide)

sequence

! overlaplgthAN :Refers to the length of an overlapping gene (nucleotide) sequence

Table PONDRS includes the results predicted by the predictor as follows. Pondr_VL3 was used in this study but Pondr_VLXT and Pondr_VSL1 were added for future research.

! Prot_ position : Refers to position of amino acid sequence

! Pondr_VL3 : Refers to PONDR VL3

! Pondr_VLXT : Refers to PONDR VLXT

! Pondr_VSL1: Refers to PONDR VSL1

Each of the four tables shown in Figure 9 includes a primary key, identified by bold font.

A primary key is used to uniquely identify tuples (rows) in a table and can be one or more attributes in the table. We have linked the tables by foreign keys as shown in Figure 9.

35 VIRUS database construction:

SQL commands were used to create tables, rows and columns as follows:

create table VIRUS_DES ( class text not null, id varchar (20) not null, name text not null, taxo text not null, gi text not null, taxid text not null, lgthgenome text not null, Primary key (id) );

create table CDS ( virusid varchar(30) not null, cdsprotid varchar(30) not null, cdsgi varchar (30) not null, cdssense text not null, cdscomplete text not null, cdsproduct text not null, cdsbegAN text not null, cdsendAN text not null, cdslgthAN text not null, cdsfullseqAA text not null, cdsfullseqAN text not null, Primary key (virusid, cdsgi, cdsprotid), foreign key (virusid) references VIRUS (id) );

36

create table OVERLAP ( overlapid varchar (30) not null, cdsprotid varchar (30) not null, overlap_begAA text, overlap_endAA text, overlap_lgthAA text, overlap_seqAA text, overlap_begAN text, overlap_endAN text, overlap_lgthAN text, Primary key (cdsprotid, overlapid), foreign key (cdsprotid) references CDS (virusid, cdsgi, cdsprotid) );

create table PONDRS ( cdsprotid varchar (30) not null, prot_position int (11), pondr_VLXT float, pondr_VL3 float, pondr_VSL1 float, Primary key (cdsprotid, prot_position), foreign key (cdsprotid) references CDS (virusid, cdsprotid, cdsgi) );

37 Database implementation

Our data model was implemented in the open source relational database management system MySQL on a linux platform as shown in Figure 10.

Figure 10. Physical Schema Of DNA_VIRUS Database

Tables in DNA Virus database CDS () OVERLAP () PONDRS() VIRUS_DES()

CDS table Field Type Null Key Default Extra virusid varchar(30) Primary cdsprotid varchar(30) Primary cdsgi varchar(30) Primary overlapid varchar(30) Primary cdssense text cdscomplete text cdsproduct text cdsbegAN text cdsendAN text cdslgthAN text cdsfullseqAA text cdsfullseqAN text

OVERLAP Table Field Type NullKey DefaultExtra overlapid varchar(30) Primary cdsprotid varchar(30) Primary overlap_begAA text yes null overlap_endAA text yes null overlap_lgthAA text yes null overlap_seqAA text yes null overlap-begAN text yes null overlap_endAN text yes null overlap_lgthAN text yes null

38 PONDRS Table Field Type Null Key DefaultExtra Cdsprotid varchar(30) Prot_position int(11) 0 Pondr_VLXT float Yes null Pondr_VL3 float Yes null Pondr_VSLI float yes null

VIRUS_DES Table Field Type Null Key DefaultExtra class text virusid varchar(20) primary name text taxo text gi text taxid text lgthgenome text

Populating and query the database

Data were first filtered to remove nucleotide redundancy and then used to populate all tables of the VIRUS database except PONDRS table. PONDRS table is populated after protein prediction. Structured query language (SQL) was used to

communicate with the database, do queries and extract the data that are stored in the

database.

Database queries

Before connecting the database to PONDRs we performed a number of queries to

test the database and extract information for protein sequence analysis. Examples of

queries are shown in Figure 11.

39 Figure 11. Examples of queries

mysql> use VIRUS; Database changed mysql> show tables; +------+ | Tables_in_VIRUS | +------+ | CDS | | OVERLAP | | PONDRS | | VIRUS_DES | +------+ 4 rows in set (0.00 sec) mysql> select overlapid from OVERLAP;

+------+ | overlapid | +------+ | OVERLAP; NC_001366-1(2) | | OVERLAP; NC_001409-2(3) | | OVERLAP; NC_001409-3(2) | | OVERLAP; NC_001560-2(3) | | OVERLAP; NC_001574-3(5) | | OVERLAP; NC_001574-5(3) | | OVERLAP; NC_001633-1(2) | | OVERLAP; NC_001633-2(1) | | OVERLAP; NC_001633-3(2) | | OVERLAP; NC_001641-1(2) | | OVERLAP; NC_001641-2(1) | | OVERLAP; NC_001642-1(2) | | OVERLAP; NC_001642-2(1) | | OVERLAP; NC_001658-3(4) | | OVERLAP; NC_001658-4(3) | | OVERLAP; NC_001719-1(3) | | OVERLAP; NC_001719-4(3) | | OVERLAP; NC_001719-7(3) | | OVERLAP; NC_001749-1(2) | | OVERLAP; NC_001749-2(1) | | OVERLAP; NC_001915-2(1) | | OVERLAP; NC_001927-1(2) | | OVERLAP; NC_001927-2(1) | | OVERLAP; NC_001990-1(2) | | OVERLAP; NC_001990-2(1) | | OVERLAP; NC_002035-1(2) | | OVERLAP; NC_000939-4(5) | | OVERLAP; NC_000939-5(4) | | OVERLAP; NC_002199-2(3) | | OVERLAP; NC_002199-3(2) | | OVERLAP; NC_002199-4(2) | | OVERLAP; NC_002200-2(3) | | OVERLAP; NC_002200-3(2) | | OVERLAP; NC_001434-9(10) | | OVERLAP; NC_001434-10(9) |

40 | OVERLAP; NC_001552-2(3) | | OVERLAP; NC_001552-3(2) | | OVERLAP; NC_001498-2(3) | | OVERLAP; NC_001498-3(2) | | OVERLAP; NC_002534-3(4) | | OVERLAP; NC_002534-4(3) | | OVERLAP; NC_002534-5(4) | | OVERLAP; NC_002568-2(4) | | OVERLAP; NC_002568-4(2) | | OVERLAP; NC_003093-5(6) | | OVERLAP; NC_003093-6(5) | | OVERLAP; NC_003448-1(2) | | OVERLAP; NC_003448-2(1) | | OVERLAP; NC_003481-3(4) | | OVERLAP; NC_003481-4(3) | | OVERLAP; NC_003487-3(4) | | OVERLAP; NC_003487-4(3) | | OVERLAP; NC_003499-5(6) | | OVERLAP; NC_003499-6(5) | | OVERLAP; NC_003532-4(5) | | OVERLAP; NC_003532-5(4) | | OVERLAP; NC_002035-2(1) | | OVERLAP; NC_003608-1(3) | | OVERLAP; NC_003608-6(7) | | OVERLAP; NC_003608-7(6) | | OVERLAP; NC_003627-4(6) | | OVERLAP; NC_003627-6(4) | | OVERLAP; NC_003725-2(3) | | OVERLAP; NC_003725-3(2) | | OVERLAP; NC_003768-1(2) | | OVERLAP; NC_003771-1(2) | | OVERLAP; NC_003771-2(1) | | OVERLAP; NC_003809-1(2) | | OVERLAP; NC_003809-2(1) | | OVERLAP; NC_004063-1(2) | | OVERLAP; NC_004063-2(1) | | OVERLAP; NC_004146-1(3) | | OVERLAP; NC_004146-3(1) | | OVERLAP; NC_004178-1(2) | | OVERLAP; NC_004178-2(1) | | OVERLAP; NC_004267-1(2) | | OVERLAP; NC_004267-2(1) | | OVERLAP; NC_004366-3(4) | | OVERLAP; NC_004366-4(3) | | OVERLAP; NC_004730-4(5) | | OVERLAP; NC_004730-5(4) | | OVERLAP; NC_004324-2(3) | | OVERLAP; NC_004324-3(2) | | OVERLAP; NC_005094-1(2) | | OVERLAP; NC_005094-2(1) | | OVERLAP; NC_005339-2(3) | | OVERLAP; NC_005339-3(2) | | OVERLAP; NC_005339-4(2) | | OVERLAP; NC_001915-1(2) | | OVERLAP; NC_003768-2(1) | | OVERLAP; NC_005339-2(4) | | OVERLAP; NC_001552-3(4) |

41 | OVERLAP; NC_001552-4(3) | | OVERLAP; NC_001560-3(2) | | OVERLAP; NC_002534-4(5) | | OVERLAP; NC_001633-2(3) | | OVERLAP; NC_001366-2(1) | | OVERLAP; NC_003608-3(1) | | OVERLAP; NC_001719-3(1) | | OVERLAP; NC_001719-3(4) | | OVERLAP; NC_001719-3(7) | | OVERLAP; NC_005899-1(2) | | OVERLAP; NC_005899-2(1) | +------+ 97 rows in set (0.02 sec)

Prediction of proteins encoded by overlapping genes

Amino acid sequences encoded by overlapping genes were converted into fasta format. Fasta format start with a title line which starts with a “>” symbol followed by lines of amino acid sequence data. The length of fasta formatted amino acid sequence data is 60 amino acid. An example of fasta format is shown in Figure 12.

Figure 12. Example of fasta format of protein sequences

>NP_690838\ MTNLQDQTQQIVPFIRSLLMPTTGPASIPDDTLEKHTLRSETSTYNLTVGDTGSGLIVFF PGFPGSIVGAHYTLQSNGNYKFDQMLLTAQNLPASYNYCRLVSRSLTVRSSTLPGGVYAL NGTINAVTFQGSLSELTDVSYNGLMSATANINDKIGNVLVGEGVTVLSLPTSYDLGYVRL GDPIPAIGLDPKMVATCDSSDRPRVYTITAADDYQFSSQYQAGGVTITLFSANIDAITSL SIGGELVFQTSVQGLILGATIYLIGFDGTAVITRAVAADNGLTAGTDNLMPFNIVIPTSE ITQPITSIKLEIVTSKSGGQAGDQMSWSASGSLAVTIHGGNYPGALRPVTLVAYERVATG SVVTVAGVSNFELIPNPELAKNLVTEYGRFDPGAMNYTKLILSERDRLGIKTVWPTREYT DFREYFMEVADLNSPLKIAGAFGFKDIIRALRRIAVPVVSTLFPPAAPLAHAIGEGVDYL LGDEAQAASGTARAASGKARAASGRIRQLTLAADKGYEVVANLFQVPQNPVVDGILASPG ILRGAHNLDCVLREGATLFPVVITTVEDAMTPKALNSKMFAVIEGVREDLQPPSQRGSFI RTLSGHRVYGYAPDGVLPLETGRVYTVVPIDGVWDDSIMLSKDPIPPIVGSSGNLAIAYM DVFRPKVPIHVAMTGALNAYGEIENVSFRSTKLATAHRLGLKLAGPGAFDVNTGSNWATF IKRFPHNPRDWDRLPYLNLPYLPPNAGRQYDLAMAASEFKETPELESAVRAMEAAANVDP LFQSALSVFMWLEENGIVTDMANFALSDPNAHRMRNFLANAPQAGSKSQRAKYGTAGYGV EARGPTPEGAQREKDTRISKKMETMGIYFATPEWVALNGHRGPSPGQLKYWQNTREIPDP NEDYLDYVHAEKSRLASEGQILRAATSIYGAPGQAEPPQAFIDEVAKVYEVNHGRGPNQE QMKDLLLTAMEMKHRNPRRAPPKPKPKPNVPTQRPPGRLGRWIRAVSDEDLE

42 To study and analyze protein sequences that are encoded by overlapping genes we ran PONDRs to predict the entire amino acid sequence of each sample and generate output data of PONDR_VL3. Using the output data we populate the table PONDR in database DNA_VIRUS using the Perl script given in Appendix 1.

Extraction of proteins encoded by overlapping genes

Next a Perl script was written to extract the predicted proteins of the overlapping genes. A sample output is shown in Figure 13. At the top of the output in Figure 13, the virus identification numbers NC_000939-4(5) and NC_000939-5(4) are shown followed by the select statements referring to the overlap locations. Next the amino acid sequence on the overlapping protein pair is given followed by the position of the amino acid on

sequence 1, the residual values of the amino acids in both sequences, and the position of

the amino acid on sequence 2. The residual value equal or greater than 0.5 indicates a

predicted disorder and less than 0.5 indicates order.

43 Figure 13. A sample output

OVERLAP; NC_000939-4(5) OVERLAP; NC_000939-5(4) select * from OVERLAP where overlapid = "OVERLAP; NC_000939-5(4)" select * from PONDRS where cdsprotid = "NP_051033" AND prot_position >= 44 AND prot_position <= 173 130 rows returned select * from PONDRS where cdsprotid = "NP_051034" AND prot_position >= 1 AND prot_position <= 130 130 rows returned 130

seq1pos O/d O/d seq2pos Amino acids: K M 44 O (0.168255) d (0.628742) 1 Amino acids: W E 45 O (0.163555) d (0.626855) 2 Amino acids: K N 46 O (0.15771) d (0.624686) 3 Amino acids: I S 47 O (0.151991) d (0.621748) 4 Amino acids: P Q 48 O (0.147354) d (0.61707) 5 Amino acids: K T 49 O (0.143412) d (0.610165) 6 Amino acids: Q G 50 O (0.140759) d (0.604253) 7 Amino acids: G V 51 O (0.138932) d (0.601007) 8 Amino acids: F L 52 O (0.139971) d (0.59991) 9 Amino acids: Y C 53 O (0.142626) d (0.599018) 10 Amino acids: A P 54 O (0.145522) d (0.596876) 11 Amino acids: P N 55 O (0.145944) d (0.593222) 12 Amino acids: I R 56 O (0.14448) d (0.590186) 13 Amino acids: D C 57 O (0.144306) d (0.589928) 14 Amino acids: V Q 58 O (0.148619) d (0.592494) 15 Amino acids: K V 59 O (0.158146) d (0.593509) 16 Amino acids: F C 60 O (0.168943) d (0.594164) 17 Amino acids: V S 61 O (0.179586) d (0.593942) 18 Amino acids: L H 62 O (0.187421) d (0.594516) 19

Our study included protein prediction for overlapping and non-overlapping regions. The focus of this study was extracting the predicted proteins of the overlapping regions. We also performed some protein prediction in non-overlapping regions.

44 IV. Results

The ordered or disordered amino acids in the proteins encoded by overlapping genes were predicted by PONDR VL3 and the results were obtained. The above predictions include both overlapping and non-overlapping regions of proteins. As mentioned earlier we were given 52 protein pairs mentioned in Table 2 to analyze.

Overlapping regions of protein pairs

First, the results for the overlapping regions of protein pairs were considered. The output of PONDR VL3 for one of these protein pairs, identified by NC_000939-4(5) and

NC_000939-5(4), in the overlapping region is presented in Appendix 2. In this output, the first section includes SQL statements to extract the encoded proteins. These statements first refer to the protein pair identifier followed by other identifications. At the end of this section we can read the number of amino acid pairs, which is 130 in this case. In the second section of the output that follows the dotted lines, amino acid names, sequence positions and PONDR VL3 predictions for both sequences in the overlapping region are shown. For example, the first amino acid pair shown in Appendix 2 is K and M. The K amino acid is at position 44 on sequence 1 and the M amino acid is at position 1 on sequence 2. The prediction is that sequence 1 is ordered, identified by (O) with the amino acid residual value of 0.168255 and sequence 2 is disordered, identified by (d) with the amino acid residual value of 0.628742. The results in section 2 include 130 amino acid pairs.

At the end of the rows for 130 pairs of amino acid the total number of predicted disordered and ordered amino acids for sequence 1 and sequence 2 are calculated and shown followed by their respective percentages. The total number of disordered (d)

45 amino acids on sequence 1 is given as19. This is followed by the total number of amino

acids on sequence 1 which are not disordered (O) and is 111. Same data for sequence 2

follow. After that the percents of (d) and (O) on each sequence are given. Finally, the last five lines in Appendix 2 include numbers related to pair analysis where both sequences

are considered.

The results indicate that out of the total overlap length of 130, there are 63 amino

acid pairs where at least one amino acid on either sequence is (d). The rest 67 amino acid

pairs are (O-O), i.e., ordered on both sequence. As shown in Appendix 2, from the 63

amino acid pairs with at least one disorder, 0 is (d-d), 19 are (d-O) and 44 are (O-d). To

clarify the pair analysis, the following example is useful. Consider two sequences shown

below. Here amino acid sequence 1 is OOOddOdO and amino acid sequence 2 is

ddOddOdd. If we consider these two sequences in pair opposite to each other like:

amino acid sequence 1 is OOOddOdO amino acid sequence 2 is ddOddOdd

Then the pair analysis would be as follows:

Number of amino acid pairs where both are ordered (O-O) 2 Number of amino acid pairs where there is at least one disorder (d-O, O-d, d-d) 6 Number of amino acid pairs where both are disordered (d-d) 3

The results of order and disorder predictions for the protein pair in Appendix 2

were imported to Excel spreadsheet together with the respective protein identifiers. In our

spreadsheet, the row that belongs to the protein pair in Appendix 2 would look as shown

in Figure 14.

46

Figure 14. Results of protein prediction in Excel Spreadsheet

In our Excel spreadsheet we adopted the following abbreviations:

Abbreviation Description P-ND Amino acid pairs where both ordered (O-O) P-BSD Amino acid pairs where both disordered (d-d) Amino acid pairs where amino acid on sequence 1 is disordered and P-SQ1D amino acid on sequence 2 is ordered (d-O) Amino acid pairs where amino acid on sequence 2 is disordered and P-SQ2D amino acid on sequence 1 is ordered (O-d) P-Total Total number of amino acid pairs in overlap

The generated output for 52 protein pairs by PONDR VL3 were imported to

Excel spreadsheet. The results are reported in Appendix 3. The row related to the sample in Appendix 2 is shaded.

The percentages of O-O, d-d, d-O and O-d for each overlapping protein pair were calculated in Excel as shown in Figure 15. In this figure, P-TD refers to the percent of amino acid pairs in the overlap where there is at least one disorder (d-d, d-O and O-d). P-

TD is equal to the sum of P-BSD, P-SQ1D and P-SQ2D. The sum of P-ND and P-TD should be 100%.

Figure 15. Percentages of order and disorder calculated by Excel Spreadsheet

47 These percentages were plotted as a bar chart as shown in Figure 16. The x-axis in

Figure 16 is the number of overlapping protein pairs (52). In the chart in Figure 16 we

start with 22 protein pairs that have no O-O, i.e., the amino acids pairs are either d-d, d-O or O-d. The 23rd protein pair has 5.5% O-O and the remaining 94.5% has at least one disorder (d-d, d-O or O-d ). As is observed in Figure 16 the percentage of O-O increases

until it becomes 100% in 50th protein pair. In Figure 17 the breakdown of percentages of

O-d, d-O, d-d and O-O for all overlapping protein pairs are shown.

Figure 16. Analysis of Order-Disorder for Overlap Proteins

100.00

90.00

80.00 At least one disordered 70.00 Both ordered

60.00

50.00 Percent 40.00

30.00

20.00

10.00

0.00 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 No. of Overlapping Protein Pairs

48 Figure 17. Analysis of Overlap Proteins with Disorder Breakdown

100.00

90.00

80.00

70.00

60.00

50.00

40.00 Percent of pairs 30.00 O-D D-O D-D 20.00 O-O

10.00

0.00 1 3 5 7 9 111315171921232527293133353739414345474951 No. of Overlapping Protein Pairs

Non-overlapping regions of proteins

We then considered the output of PONDR VL3 for non-overlapping regions of 52

protein pairs. A sample of PONDR VL3 output for two proteins with protein numbers

NP_051033 and NP_051034 is presented in Appendix 4. These two proteins belong to

the protein pair to which we referred in Appendix 2. Data in Appendix 4 shows the

residual values for each amino acids in the entire protein sequence, i.e., overlapping and

non-overlapping regions for the two proteins. The overlapping region is shaded in

Appendix 4. Out of 52 protein pairs, or 104 samples, 7 samples generated repeating order

and disorder information which was redundant. Therefore, those 7 samples were removed

from the pool for the analysis of non-overlapping region. The results generated by

49 PONDR VL3 for 97 proteins were imported to Excel spreadsheet and tabulated. The tabulated results in our spreadsheet for two protein pairs would look as shown in Figure

18. This figure shows the length, orders and disorders for the proteins in the overlap region (which was earlier discussed), followed by the similar information for the entire sequence and non-overlap region.

Figure 18. Results of protein prediction for non-overlap region

The generated results for non-overlap regions for 97 proteins are reported in

Appendix 5. The row related to the sample in Appendix 4 is shaded. Using the data in

Appendix 5 we calculated the fraction of ordered amino acids (O) in each protein sequence. We sorted data to the proteins with the higher fraction of (O) in a descending order. A plot of the data is shown in Figure 19. A global fraction of (O) in the entire pool of 97 proteins was also calculated as 0.77.

Bootstrapping

The results generated above were based on a dataset with 97 viral proteins or 52 protein pairs. To test the reliability of our results we applied bootstrapping. This is a statistical method used to examine if a particular dataset is biased. We used boostrapping in two cases, first for the fraction of O-O pairs in the overlapping region and second the fraction of disorder in the entire sequence. In our boostrapping, we used a repeated

50 random sampling with replacement from our original sample of 52 protein pairs or 97

proteins to provide 10,000 new pseudoreplicate samples, from which sampling variance and margin of error could be estimated at a given confidence level.

51 Figure 19. Fraction of Ordered Amino Acids in the Non-overlap Region of Proteins

1.0

0.8

0.6 Non-overlap Region

0.4

0.2

Order inFraction of

0.0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 Number of Proteins

52 V. Discussion

Overlapping genes are adjacent genes which share a portion of their nucleotide sequence. They are often observed in compact genome of viruses, prokaryotic genome, and organelles like mitochondria. They may also be present in human and other mammalian genome. These organisms take advantage of overlapping genes to produce new proteins without increasing the size of genome. Overlapping genes produce different proteins. A major work in post genomic era is large-scale study of structures and functions of proteins. Although, in general, it is assumed that 3-D structure of a protein determines the function of proteins, but many proteins or regions of proteins may function in the absence of 3-D structure. The term disordered is used to describe these proteins. Based on a large number of studies, biological functions depend on both ordered and disordered proteins.

Disordered regions of proteins can be predicted using specific amino acid composition of these regions. There are several programs that can identify these disordered regions. PONDR VL3, a neural network predictor that uses amino acid sequence data to predict disorder in a given region, was used in this study.

In the results section we performed studies on 97 proteins (52 protein pairs) encoded by overlapping gene to decide the order or disorder of amino acids in the sequence of each protein. The length of amino acid sequence in overlapping regions for the above proteins were at least 31 and at most 626. Also we analyzed each protein sequence for the percentage of disordered amino acids in its overlapping and non- overlapping regions. The entire length of amino acid sequence for the above proteins,

53 including the overlapping and non-overlapping regions, were at least 62 and at most

2303.

As mentioned earlier, based on our hypothesis, most often, in a pair of proteins

encoded by overlapping genes at least one is disordered (unstructured). This is believed

to be attributed to the creation of these proteins by overprinting the sequence of a pre-

existing gene. The overprinting mechanism may impose a constraint where the genetic

code would not allow encoding of two structured proteins in different reading frames.

Figure 20 shows that there are only 3 protein pairs out of 52, indicated by 100% blue bars, where there are no disorder on either sequence of the pair. Moreover, as highlighted in Figure 20, for about 39 protein pairs out of 52, the length of blue bar

(percent of O-O) is less than 50%.

Figure 20 shows that there are 22 protein pairs where there is no O-O (indicated by 100% red bar). There are 40 pairs where there are some O-O amino acid pairs

(indicated by blue bar). The total number of amino acid pairs in the overlapping region for all the proteins under study (52 pairs) is 7219 and the total number of amino acid pairs which are ordered (O-O) in the overlapping regions for all proteins is 2014.

Therefore, the global fraction of O-O pairs in the overlapping region would be obtained by dividing 2014 by 7219 which is 0.28. Bootstrapping of 10,000 random samples using the data on our 52 protein pairs shows that this fraction, i.e., fraction of O-O pairs, would also be 0.28 with 95% confidence.

The above results indicate that according to our hypothesis, for 52 pairs of proteins encoded by overlapping genes that were studied, most often, at least one is disordered and the O-O pairs are less than 30%.

54

Figure 20. Overlapping protein pairs with more than 50% disorder

100.00

90.00

80.00 At least one disordered 70.00 Both ordered

60.00

50.00

Percent 40.00

30.00

20.00

10.00

0.00 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 No. of Overlapping Protein Pairs

Figure 21 shows that out of 97 proteins, there are 80 proteins for which the

percent of ordered amino acids (O) in the non-overlapping region, indicated by red bars,

is greater than 50% (as highlighted). Data in Appendix 5 indicate that the total number of

ordered amino acids in the non-overlapping region is 25428 and the total length of the

non-overlapping region is 32946. This will result in a fraction of ordered amino acids in

the non-overlapping region equal to 0.77. This is another indication that the ordered

amino acids are mostly associated with the non-overlapping region while the disorderd

amino acids are prevalent in overlapping region. This is another support for our hypothsis.

Further analysis was performed on the entire sequence of proteins which included both overlapping and non-overlapping regions. The fraction of disorder in the entire

55

Figure 21. Fraction of Ordered Amino Acids in the Non-overlap Region of Proteins

1.0

0.8

0.6 Non-overlap Region

0.4

0.2 Fraction of Order inFraction of

0.0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 Number of Proteins

sequence of proteins is shown in Figure 22. As shown in Appendix 5 the total length of the entire sequences of 97 proteins under study is 47543 and the number of disordered amino acids in these sequences is 14476. This will result in a fraction of disordered amino acids in the entire sequence equal to 0.30. Bootstrapping of 10,000 random samples using the data for the entire sequence of 97 proteins shows that the fraction of disorder in the entire sequence would be 0.31 with 95% confidence.

56 Figure 22. Fraction of Disordered Amino Acids in the Entire Sequence of Proteins

1.0

0.8

0.6

0.4

0.2 Fraction ofDisorder in Entire Sequence

0.0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 Number of Proteins

57 VI. Conclusions

In our study we focused on the proteins encoded by overlapping genes. These proteins are produced due to frame shift phenomenon where changes in the reading frame of a nucleotide sequence leads to the production of different amino acid sequences.

Unlike most proteins that have a 3-D structure, the majority of proteins that are

encoded by overlapping genes, are unstructured and thus, called disordered. Although, in

general, it is assumed that 3-D structure of a protein determines the function of proteins, but based on a large number of studies, biological functions depend on both ordered and disordered proteins.

We developed a method to predict and analyze the proteins encoded by overlapping genes. Our method included design, construction and implementation of a database, populating and query of the database and finally prediction of proteins encoded by overlapping genes using the database and the protein predictor PONDR VL3. Using our method we could predict the order-disorder of amino acids in the sequence of 97 viral protein samples that were provided to us. The results we generated in this study were tabulated and analyzed to provide the number and fraction of ordered and disordered amino acids in the overlapping, non-overlapping regions and the entire sequence of 97 protein samples under study.

The objective of our study was to investigate our hypothesis that most often, in a

pair of proteins encoded by overlapping genes at least one is disordered (unstructured). In

another word, in the sequence of proteins produced by overlapping genes, an ordered amino acid on one sequence corresponds to a disordered amino acid on the other sequence in most of the cases. This is believed to be attributed to constraints where the

58 genetic code would not allow encoding of two structured proteins in different reading

frames.

We considered 97 samples given to us as 52 pairs in one set of analysis and as 97

protein sequences in a different analysis. When each of the 52 protein pairs were considered, we showed that most of the amino acid pairs facing each other on the protein sequences had at least one disorder for most cases. There were only 3 protein pairs out of

52 where there were no disorder on either sequence of the protein pair. On the other hand, there were 22 protein pairs out of 52 where there were no O-O amino acid pair and in 39 protein pairs, the percent of O-O amino acid pairs was less than 50%. The global fraction

of O-O pairs in the pool of overlapping regions of 52 protein pairs was 0.28.

Bootstrapping of 10,000 random samples with 95% confidence also resulted in the same fraction. The pair analysis of proteins encoded by overlapping genes, supported our

hypothesis

When 97 proteins were considered one sequence at a time, there were 80 proteins

for which the percent of ordered amino acids in the non-overlapping region, was greater

than 50%. The fraction of ordered amino acids in the pool of 97 proteins in their non-

overlapping regions was calculated to be 0.77. This is another indication that the ordered

amino acids are mostly associated with the non-overlapping region while the disorderd

amino acids are prevalent in overlapping region. This is another support for our hypothsis.

59 VII. Recommendations for future work

We recommend to expand this study by applying the methodology developed in

this work to new datasets of different organisms. Moreover, amino acid composition of

the overlapping genes could be studied to see which amino acids promote order or

disorder.

It has been shown that overlapping genes may play an important role as

transcriptional and translational regulators of gene expression, which in turn, determine

the function of proteins. In the future protein product of overlapping genes could be

studied to see what kind of function they might be involved.

In this study we observed that an average 28% of the overlaps were O-O. The

relationship between the function of protein and percent O-O in the overlap can be studied. We also saw a limited number that had 100% O-O in their overlap. The location of this overlap can be studied to see if it occurred in the middle or at either ends of the entire protein sequence. The work on 100% O-O in overlap can be further extended by a homologue study using blast search.

Our hypothesis can be studied using protein products of overlapping genes in related species and comparisons can be made using the results of our viral protein study.

Many human diseases are associated with overlapping genes because of

anomalous sequence features in this region. Moreover, it has been reported that 80% of

cancer-associated proteins predicted to have large regions of disorder. Study of protein

products of this region could shed further light on the possible role of disordered proteins

produced by overlapping genes in human diseases

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65 Appendix 1

Perl script for populating the table PONDR in DNA_VIRUS database

#! /usr/bin/perl

use DBI; use FileHandle; my ($dbh, $sth); my ($user_name)="mkhosrav"; my ($password)="4QqM37eF6s"; my $table = "PONDRS"; my $db = "DNA_VIRUS"; my $data_file="DNAss_linout.txt";

$dbh=DBI-

>connect("DBI:mysql:database=DNA_VIRUS;host=localhost",$mkhosrav,$4QqM37eF6 s,{RaiseError=>1}); open(DAT,$DNAss_linout.txt)|| die("Could not open file: $!");

@lines = ; close DAT; foreach $line(@line){ if ($line= ~ m/#/)

{

$line= ~ S/#//;

$cdsprotid = $line;} elsif ($line!~ M/\S/)

{

@data=split (" ", $line); print $data[0]; print $data[1]; print $data[2];

$query="insert into PONDRS (cdsprotid, prot_position, PONDR_VL3) values

($cdsprotid, $data[0], $data[2])";

$sth= dbh->prepare($query);

$sth->execute();

}

}

$sth->finish();

$dbh->disconnect();

Appendix 2

A sample of output for prediction of order and disorder of proteins in the overlapping region

OVERLAP; NC_000939-4(5) OVERLAP; NC_000939-5(4) select * from OVERLAP where overlapid = "OVERLAP; NC_000939-5(4)" select * from PONDRS where cdsprotid = "NP_051033" AND prot_position >= 44 AND prot_position <= 173 130 rows returned select * from PONDRS where cdsprotid = "NP_051034" AND prot_position >= 1 AND prot_position <= 130 130 rows returned 130 seq1pos O/d O/d seq2pos ------Amino acids: K M44 O (0.168255) d (0.628742) 1 Amino acids: W E45 O (0.163555) d (0.626855) 2 Amino acids: K N46 O (0.15771) d (0.624686) 3 Amino acids: I S47 O (0.151991) d (0.621748) 4 Amino acids: P Q48 O (0.147354) d (0.61707) 5 Amino acids: K T49 O (0.143412) d (0.610165) 6 Amino acids: Q G50 O (0.140759) d (0.604253) 7 Amino acids: G V51 O (0.138932) d (0.601007) 8 Amino acids: F L52 O (0.139971) d (0.59991) 9 Amino acids: Y C53 O (0.142626) d (0.599018) 10 Amino acids: A P54 O (0.145522) d (0.596876) 11 Amino acids: P N55 O (0.145944) d (0.593222) 12 Amino acids: I R56 O (0.14448) d (0.590186) 13 Amino acids: D C57 O (0.144306) d (0.589928) 14 Amino acids: V Q58 O (0.148619) d (0.592494) 15 Amino acids: K V59 O (0.158146) d (0.593509) 16 Amino acids: F C60 O (0.168943) d (0.594164) 17 Amino acids: V S61 O (0.179586) d (0.593942) 18 Amino acids: L H62 O (0.187421) d (0.594516) 19 Amino acids: T T63 O (0.195137) d (0.593076) 20 20seq2 disordered Amino acids: P T64 O (0.201413) d (0.589753) 21 Amino acids: H Y65 O (0.209206) d (0.58643) 22 Amino acids: I I66 O (0.218029) d (0.583107) 23 23seq2 disordered Amino acids: S R67 O (0.228422) d (0.579783) 24 Amino acids: E E68 O (0.238379) d (0.57646) 25 25seq2 disordered Amino acids: R S69 O (0.248252) d (0.573137) 26 Amino acids: A S70 O (0.258021) d (0.569814) 27 Amino acids: Q G71 O (0.2704) d (0.566491) 28 Amino acids: V Q72 O (0.284319) d (0.563168) 29 Amino acids: R G73 O (0.298677) d (0.559845) 30 Amino acids: G G74 O (0.310698) d (0.556522) 31 31seq2 disordered Amino acids: V R75 O (0.320918) d (0.553199) 32 Amino acids: V Q76 O (0.329199) d (0.549876) 33 Amino acids: K A77 O (0.335734) d (0.546553) 34 Amino acids: L C78 O (0.34063) d (0.54323) 35

Amino acids: V R79 O (0.34507) d (0.539907) 36 Amino acids: D F80 O (0.349925) d (0.536584) 37 Amino acids: S T81 O (0.354849) d (0.533261) 38 Amino acids: R R82 O (0.358642) d (0.529938) 39 39seq2 disordered Amino acids: D F83 O (0.360413) d (0.526615) 40 Amino acids: L V84 O (0.361245) d (0.523292) 41 Amino acids: S T85 O (0.363247) d (0.519969) 42 Amino acids: P Q86 O (0.366261) d (0.516646) 43 Amino acids: S P87 O (0.370165) d (0.513323) 44 Amino acids: R R88 O (0.371909) O (0.4523) 45 Amino acids: E V89 O (0.369587) O (0.38784) 46 Amino acids: L V90 O (0.361249) O (0.320718) 47 Amino acids: Y S91 O (0.350751) O (0.306974) 48 Amino acids: R E92 O (0.339616) O (0.294188) 49 Amino acids: S Q93 O (0.331223) O (0.279732) 50 Amino acids: K G94 O (0.3229) O (0.265332) 51 Amino acids: E I95 O (0.316261) O (0.251962) 52 Amino acids: F Q96 O (0.307946) O (0.241968) 53 Amino acids: N Y97 O (0.300035) O (0.235196) 54 Amino acids: I R98 O (0.291681) O (0.229073) 55 Amino acids: G S99 O (0.284926) O (0.222146) 56 Amino acids: H W100 O (0.277606) O (0.215086) 57 Amino acids: G L101 O (0.269677) O (0.207027) 58 Amino acids: L S102 O (0.258508) O (0.197978) 59 Amino acids: V D103 O (0.24556) O (0.188037) 60 Amino acids: I R104 O (0.23223) O (0.177213) 61 Amino acids: E G105 O (0.221738) O (0.168888) 62 Amino acids: G F106 O (0.214016) O (0.164301) 63 Amino acids: S P107 O (0.209983) O (0.163215) 64 Amino acids: Q A108 O (0.207681) O (0.163626) 65 Amino acids: L T109 O (0.206839) O (0.162108) 66 Amino acids: P L110 O (0.204695) O (0.159318) 67 Amino acids: F L111 O (0.201722) O (0.155536) 68 Amino acids: C S112 O (0.197002) O (0.152579) 69 Amino acids: L T113 O (0.192496) O (0.150301) 70 Amino acids: P S114 O (0.187574) O (0.14749) 71 Amino acids: V G115 O (0.182377) O (0.144328) 72 Amino acids: G G116 O (0.176845) O (0.141105) 73 Amino acids: D L117 O (0.173314) O (0.137687) 74 Amino acids: Y S118 O (0.172612) O (0.134025) 75 Amino acids: P T119 O (0.173061) O (0.129457) 76 Amino acids: L T120 O (0.173404) O (0.123551) 77 Amino acids: Q I121 O (0.174493) O (0.116544) 78 Amino acids: F R122 O (0.178005) O (0.110201) 79 Amino acids: E G123 O (0.184458) O (0.106322) 80 Amino acids: V H124 O (0.191875) O (0.104729) 81 Amino acids: T G125 O (0.19946) O (0.10448) 82 Amino acids: V V126 O (0.205579) O (0.104506) 83 Amino acids: L A127 O (0.212177) O (0.104315) 84 Amino acids: Q V128 O (0.217475) O (0.103776) 85 Amino acids: S T129 O (0.22331) O (0.102923) 86 Amino acids: Q I130 O (0.229232) O (0.101915) 87 Amino acids: F Q131 O (0.237641) O (0.0999731) 88 Amino acids: R G132 O (0.247765) O (0.0985853) 89 Amino acids: E D133 O (0.259101) O (0.0965168) 90 Amino acids: T S134 O (0.270505) O (0.0954874) 91

Amino acids: A K135 O (0.28191) O (0.0956794) 92 Amino acids: N S136 O (0.293314) O (0.0969503) 93 Amino acids: L L137 O (0.304719) O (0.0970088) 94 Amino acids: Y L138 O (0.316123) O (0.0951305) 95 Amino acids: S N139 O (0.327528) O (0.0923114) 96 Amino acids: T F140 O (0.338932) O (0.0914421) 97 Amino acids: S C141 O (0.350337) O (0.0920859) 98 Amino acids: V R142 O (0.361741) O (0.0944435) 99 Amino acids: E V143 O (0.373146) O (0.0976489) 100 Amino acids: W A144 O (0.38455) O (0.101804) 101 Amino acids: R Y145 O (0.395955) O (0.106974) 102 Amino acids: M D146 O (0.407359) O (0.112257) 103 Amino acids: M V147 O (0.418764) O (0.117923) 104 Amino acids: S F148 O (0.430168) O (0.124381) 105 Amino acids: S H149 O (0.441573) O (0.132517) 106 Amino acids: T H150 O (0.452977) O (0.143772) 107 Amino acids: T P151 O (0.464382) O (0.157478) 108 Amino acids: P V152 O (0.475786) O (0.173772) 109 Amino acids: L V153 O (0.487191) O (0.192083) 110 Amino acids: S Q154 O (0.498595) O (0.212542) 111 Amino acids: R S155 d (0.514991) O (0.234928) 112 Amino acids: V E156 d (0.536376) O (0.256765) 113 Amino acids: R V157 d (0.562753) O (0.278909) 114 Amino acids: S C158 d (0.589129) O (0.295554) 115 Amino acids: V H159 d (0.615506) O (0.308007) 116 Amino acids: M G160 d (0.641882) O (0.315913) 117 Amino acids: G S161 d (0.668258) O (0.324561) 118 Amino acids: A G162 d (0.694635) O (0.333676) 119 Amino acids: A P163 d (0.721011) O (0.342237) 120 Amino acids: Q A164 d (0.747388) O (0.350608) 121 Amino acids: Q T165 d (0.773764) O (0.359037) 122 Amino acids: P S166 d (0.800141) O (0.367866) 123 Amino acids: A D167 d (0.821531) O (0.377165) 124 Amino acids: M E168 d (0.838629) O (0.387531) 125 Amino acids: K I169 d (0.853569) O (0.399986) 126 Amino acids: L T170 d (0.870614) O (0.414747) 127 Amino acids: Q T171 d (0.888659) O (0.431481) 128 Amino acids: P K172 d (0.904826) O (0.45017) 129 Amino acids: N F173 d (0.919749) O (0.459772) 130 19 seq1 disordered 111 seq1 not disordered 44 seq2 disordered 86 seq2 not disordered

14.6153846153846 percent seq1 disordered 85.3846153846154 percent seq1 not disordered 33.8461538461538 percent seq2 disordered 66.1538461538461 percent seq2 not disordered 63 disordered 67 not disordered 0 both disordered 19 seq1 disordered 44 seq2 disordered

Appendix 3

Result of order and disorder prediction of proteins in the overlapping regions

Sequence 1 Sequence 2 No. of pairs

Acc number order order Protein Protein position position Overlap Overlap disorder disorder P-ND (O-O) P-BSD (d-d) identification identification identification P-SQ1D (d-O) P-SQ2D (O-d) No. of predicted No. of predicted No. of predicted No. of predicted

NC_001915-1(2) NC_001915-2(1) NP_X10000 3 133 90 41 NP_047196 1 131 22 109 41 22 68 0 NC_004178-1(2) NC_004178-2(1) NP_690837 16 149 111 23 NP_690838 1 134 15 119 23 15 96 0 NC_004267-1(2) NC_004267-2(1) NP_694621 21 139 73 46 NP_694622 1 119 25 94 37 16 57 9 NC_003771-1(2) NC_003771-2(1) NP_620541 160 485 0 326 NP_620542 1 326 165 161 161 0 0 165 NC_003768-1(2) NC_003768-2(1) NP_620538 91 182 51 41 NP_X10001 1 92 72 20 11 42 9 30 NC_001641-1(2) NC_001641-2(1) NP_042580 649 697 31 18 NP_042581 1 49 27 22 18 27 4 0 NC_001927-1(2) NC_001927-2(1) NP_047213 7 107 0 101 NP_047214 1 101 21 80 80 0 0 21 NC_001498-2(3) NC_001498-3(2) NP_056919 8 193 132 54 NP_056920 1 186 95 91 35 76 56 19 NC_002199-2(3) NC_002199-3(2) NP_054691 230 282 1 52 NP_054692 230 282 17 36 36 1 0 16 NC_002199-2(4) NC_002199-4(2) NP_X10002 9 161 153 0 NP_054693 1 153 88 65 0 88 65 0 NC_005339-2(3) NC_005339-3(2) NP_958049 244 295 52 0 NP_958050 244 295 19 33 0 19 33 0 NC_005339-2(4) NC_005339-4(2) NP_X10003 11 162 114 38 NP_958051 1 152 78 74 38 78 36 0 NC_001552-2(3) NC_001552-3(2) NP_056872 8 215 111 97 NP_056873 1 208 208 0 0 111 0 97 NC_001552-3(4) NC_001552-4(3) NP_X10004 318 369 40 12 NP_X10005 318 369 40 12 12 40 0 0 NC_002200-2(3) NC_002200-3(2) NP_054708 156 224 59 10 NP_054709 1 224 21 48 0 11 48 10 NC_001560-2(3) NC_001560-3(2) NP_041713 25 91 67 0 NP_X10006 1 67 29 38 0 29 38 0 NC_002534-3(4) NC_002534-4(3) NP_065672 184 227 0 44 NP_065673 1 44 0 44 44 0 0 0 NC_002534-4(5) NC_002534-5(4) NP_X10007 156 191 0 36 NP_065674 1 36 0 36 36 0 0 0

Sequence 1 Sequence 2 No. of pairs NC_001633-1(2) NC_001633-2(1) NP_042508 3 179 0 177 NP_042509 1 177 0 177 177 0 0 0 NC_001633-2(3) NC_001633-3(2) NP_X10008 605 657 53 0 NP_042510 1 53 0 53 0 0 53 0 NC_002035-1(2) NC_002035-2(1) NP_049324 778 857 55 25 NP_619631 1 80 80 0 0 55 0 25 NC_003809-1(2) NC_003809-2(1) NP_620678 696 797 75 27 NP_620679 1 102 3 99 24 0 75 3 NC_001749-1(2) NC_001749-2(1) NP_044335 1584 1903 50 269 NP_044336 1 320 89 230 210 30 20 59 NC_003499-5(6) NC_003499-6(5) NP_612812 268 312 41 4 NP_612813 1 45 0 45 4 0 41 0 NC_003093-5(6) NC_003093-6(5) NP_203557 226 325 14 86 NP_203558 1 100 95 5 0 9 5 86 NC_001642-1(2) NC_001642-2(1) NP_042582 421 547 107 20 NP_042583 1 127 97 30 7 84 23 13 NC_001658-3(4) NC_001658-4(3) NP_042697 63 112 0 50 NP_042698 1 50 22 28 28 0 0 22 NC_001409-2(3) NC_001409-3(2) NP_040552 356 460 105 0 NP_040553 1 105 0 105 0 0 105 0 NC_001434-10(9) NC_001434-9(10) NP_056788 14 123 110 0 NP_056787 1 110 83 27 0 83 27 0 NC_003481-3(4) NC_003481-4(3) NP_604488 69 131 33 30 NP_604489 1 63 43 20 0 13 20 30 NC_004730-4(5) NC_004730-5(4) NP_835266 71 122 0 52 NP_835267 1 52 16 36 36 0 0 16 NC_003725-2(3) NC_003725-3(2) NP_620439 72 119 0 48 NP_620440 1 48 25 23 23 0 0 25 NC_002568-2(4) NC_002568-4(2) NP_066392 900 962 42 21 NP_066394 1 63 63 0 0 42 0 21 NC_004366-3(4) NC_004366-4(3) NP_733849 6 237 232 0 NP_733850 1 232 64 168 0 64 168 0 NC_004146-1(3) NC_004146-3(1) NP_689444 900 998 99 0 NP_689446 1 99 45 54 0 45 54 0 NC_003448-1(2) NC_003448-2(1) NP_599247 893 967 75 0 NP_599248 1 75 28 47 0 28 47 0 NC_005094-1(2) NC_005094-2(1) NP_919036 901 1033 133 0 NP_919037 1 133 46 87 0 46 87 0 NC_001366-1(2) NC_001366-2(1) NP_040350 5 160 95 61 NP_X10009 1 156 0 156 61 0 95 0 NC_001990-1(2) NC_001990-2(1) NP_048059 1316 1925 546 64 NP_048060 1 610 110 500 64 110 436 0 NC_005899-1(2) NC_005899-2(1) YP_025095 32 158 114 13 YP_025096 1 127 61 66 0 48 66 13 NC_000939-4(5) NC_000939-5(4) NP_051033 44 173 19 111 NP_051034 1 130 44 86 67 0 19 44 NC_003608-1(3) NC_003608-3(1) NP_619671 4 212 51 157 NP_X10010 1 209 53 155 121 17 34 36 NC_003608-6(7) NC_003608-7(6) NP_619676 5 228 22 202 NP_619677 1 224 0 224 202 0 22 0 NC_003627-4(6) NC_003627-6(4) NP_619720 130 279 66 84 NP_619722 1 150 55 95 71 42 24 13 NC_003487-3(4) NC_003487-4(3) NP_608313 13 62 0 50 NP_608314 1 50 50 0 0 0 0 50 NC_003532-4(5) NC_003532-5(4) NP_613263 11 182 39 133 NP_613264 1 172 119 53 53 39 0 80 NC_004063-1(2) NC_004063-2(1) NP_663296 3 628 626 0 NP_663297 1 626 149 477 0 149 477 0 NC_001574-3(5) NC_001574-5(3) NP_041734 1721 1834 19 95 NP_041736 1 114 25 89 70 0 19 25 NC_001719-1(3) NC_001719-3(1) NP_043862 166 217 52 0 NP_X10011 1 52 25 27 0 25 27 0 NC_001719-3(4) NC_001719-4(3) NP_X10012 188 614 148 279 NP_043865 1 427 149 278 224 94 54 55

Sequence 1 Sequence 2 No. of pairs NC_001719-3(7) NC_001719-7(3) NP_X10013 793 877 46 39 NP_043868 1 85 66 19 0 27 19 39 NC_004324-2(3) NC_004324-3(2) NP_861408 148 178 31 0 NP_861409 1 31 28 3 0 28 3 0 Total pool of 52 protein pairs 2014 1653 2530 1022

Appendix 4

A sample of output for order and disorder predictions in the entire sequence of a protein pair

NP_051033 NP_051034 1 M 0.4263188541 1 M 0.6287424564 2 E 0.4210431874 2 E 0.6268553734 3 I 0.4103756845 3 N 0.6246856451 4 Q 0.4013533592 4 S 0.6217484474 5 S 0.3937372863 5 Q 0.6170695424 6 L 0.3872836828 6 T 0.6101647019 7 D 0.3812186420 7 G 0.6042528152 8 G 0.3759230673 8 V 0.6010074019 9 V 0.3698520362 9 L 0.5999103189 10 L 0.3629848063 10 C 0.5990181565 11 G 0.3546615839 11 P 0.5968763232 12 E 0.3473497331 12 N 0.5932222605 13 E 0.3415260613 13 R 0.5901864171 14 L 0.3376469910 14 C 0.5899282098 15 A 0.3352956772 15 Q 0.5924942493 16 I 0.3289301991 16 V 0.5935085416 17 Q 0.3169742525 17 C 0.5941638350 18 N 0.3013938367 18 S 0.5939415097 19 E 0.2880397737 19 H 0.5945159197 20 V 0.2782653272 20 T 0.5930755734 21 K 0.2688401639 21 T 0.5897526145 22 K 0.2598593235 22 Y 0.5864295959 23 I 0.2507073581 23 I 0.5831065178 24 L 0.2433348447 24 R 0.5797834992 25 L 0.2368623018 25 E 0.5764604211 26 S 0.2304195911 26 S 0.5731374621 27 H 0.2238274366 27 S 0.5698144436 28 K 0.2177044600 28 G 0.5664914250 29 T 0.2132986337 29 Q 0.5631683469 30 T 0.2100027949 30 G 0.5598453879 31 K 0.2072314024 31 G 0.5565223694 32 A 0.2048592418 32 R 0.5531993508 33 I 0.2024080008 33 Q 0.5498762727 34 L 0.1999487430 34 A 0.5465533137 35 P 0.1969751120 35 C 0.5432302356 36 L 0.1936348677 36 R 0.5399072170 37 A 0.1898535937 37 F 0.5365841985 38 P 0.1857631058 38 T 0.5332611203 39 I 0.1810358167 39 R 0.5299381614 40 S 0.1768899411 40 F 0.5266151428 41 Q 0.1739731282 41 V 0.5232921243 42 F 0.1726955622 42 T 0.5199690461 43 S 0.1711555868 43 Q 0.5166460872 44 K 0.1682546586 44 P 0.5133228302 45 W 0.1635548472 45 R 0.4523003101 46 K 0.1577104777 46 V 0.3878404200 47 I 0.1519905925 47 V 0.3207175434 48 P 0.1473541111 48 S 0.3069736660 49 K 0.1434118748 49 E 0.2941881418 50 Q 0.1407587975 50 Q 0.2797319591

51 G 0.1389324963 51 G 0.2653318942 52 F 0.1399712563 52 I 0.2519617081 53 Y 0.1426260024 53 Q 0.2419683188 54 A 0.1455216408 54 Y 0.2351964265 55 P 0.1459440589 55 R 0.2290729284 56 I 0.1444802135 56 S 0.2221457958 57 D 0.1443055123 57 W 0.2150861174 58 V 0.1486189216 58 L 0.2070267648 59 K 0.1581459790 59 S 0.1979777366 60 F 0.1689433306 60 D 0.1880372614 61 V 0.1795858145 61 R 0.1772130281 62 L 0.1874209046 62 G 0.1688884497 63 T 0.1951367855 63 F 0.1643005013 64 P 0.2014129162 64 P 0.1632147580 65 H 0.2092055678 65 A 0.1636258364 66 I 0.2180292755 66 T 0.1621076614 67 S 0.2284219116 67 L 0.1593181342 68 E 0.2383794338 68 L 0.1555356532 69 R 0.2482522130 69 S 0.1525788158 70 A 0.2580212057 70 T 0.1503012329 71 Q 0.2703996599 71 S 0.1474897563 72 V 0.2843189538 72 G 0.1443284601 73 R 0.2986767590 73 G 0.1411047876 74 G 0.3106977940 74 L 0.1376874596 75 V 0.3209180832 75 S 0.1340254992 76 V 0.3291994631 76 T 0.1294572800 77 K 0.3357338011 77 T 0.1235513091 78 L 0.3406301737 78 I 0.1165436134 79 V 0.3450701237 79 R 0.1102013364 80 D 0.3499245346 80 G 0.1063217893 81 S 0.3548491299 81 H 0.1047294140 82 R 0.3586422205 82 G 0.1044798866 83 D 0.3604131639 83 V 0.1045063809 84 L 0.3612449169 84 A 0.1043152884 85 S 0.3632472754 85 V 0.1037761867 86 P 0.3662614822 86 T 0.1029228568 87 S 0.3701654673 87 I 0.1019146219 88 R 0.3719085157 88 Q 0.0999731123 89 E 0.3695870638 89 G 0.0985852703 90 L 0.3612492979 90 D 0.0965167880 91 Y 0.3507513106 91 S 0.0954874381 92 R 0.3396156728 92 K 0.0956793651 93 S 0.3312228024 93 S 0.0969502926 94 K 0.3228998482 94 L 0.0970088318 95 E 0.3162614107 95 L 0.0951305330 96 F 0.3079462349 96 N 0.0923113525 97 N 0.3000348508 97 F 0.0914420709 98 I 0.2916814387 98 C 0.0920859054 99 G 0.2849255800 99 R 0.0944434628 100 H 0.2776061594 100 V 0.0976488590 101 G 0.2696771324 101 A 0.1018036008 102 L 0.2585083544 102 Y 0.1069739833 103 V 0.2455599755 103 D 0.1122568250 104 I 0.2322298139 104 V 0.1179232895 105 E 0.2217383534 105 F 0.1243807152 106 G 0.2140158415 106 H 0.1325165480 107 S 0.2099828124 107 H 0.1437723488

108 Q 0.2076812238 108 P 0.1574780196 109 L 0.2068393975 109 V 0.1737724692 110 P 0.2046948224 110 V 0.1920834035 111 F 0.2017216533 111 Q 0.2125422508 112 C 0.1970018744 112 S 0.2349284440 113 L 0.1924956292 113 E 0.2567650974 114 P 0.1875739843 114 V 0.2789087296 115 V 0.1823773831 115 C 0.2955537736 116 G 0.1768452674 116 H 0.3080069721 117 D 0.1733143777 117 G 0.3159132302 118 Y 0.1726116687 118 S 0.3245606124 119 P 0.1730613261 119 G 0.3336759508 120 L 0.1734036952 120 P 0.3422371447 121 Q 0.1744927913 121 A 0.3506084383 122 F 0.1780048609 122 T 0.3590371609 123 E 0.1844580024 123 S 0.3678661287 124 V 0.1918754131 124 D 0.3771648407 125 T 0.1994600743 125 E 0.3875309527 126 V 0.2055794448 126 I 0.3999863565 127 L 0.2121766210 127 T 0.4147466719 128 Q 0.2174746245 128 T 0.4314810038 129 S 0.2233098000 129 K 0.4501699507 130 Q 0.2292315364 130 F 0.4597721696 131 F 0.2376412749 132 R 0.2477651685 133 E 0.2591006458 134 T 0.2705051601 135 A 0.2819096744 136 N 0.2933141887 137 L 0.3047187030 138 Y 0.3161232173 139 S 0.3275277317 140 T 0.3389322758 141 S 0.3503367901 142 V 0.3617413044 143 E 0.3731458187 144 W 0.3845503330 145 R 0.3959548473 146 M 0.4073593616 147 M 0.4187638760 148 S 0.4301683903 149 S 0.4415729046 150 T 0.4529774189 151 T 0.4643819332 152 P 0.4757864475 153 L 0.4871909618 154 S 0.4985953867 155 R 0.5149905086 156 V 0.5363762379 157 R 0.5627526641 158 S 0.5891291499 159 V 0.6155056357 160 M 0.6418820620 161 G 0.6682584882 162 A 0.6946349144 163 A 0.7210113406 164 Q 0.7473878264

165 Q 0.7737643123 166 P 0.8001406789 167 A 0.8215308189 168 M 0.8386289477 169 K 0.8535690308 170 L 0.8706142306 171 Q 0.8886590004 172 P 0.9048259854 173 N 0.9197490811 174 F 0.9320861697 175 K 0.9425940514 176 M 0.9510643482 177 S 0.9587990642 178 L 0.9655229449 179 E 0.9714501500 180 S 0.9759386182 181 S 0.9794287682 182 K 0.9817369580 183 G 0.9837498069 184 G 0.9852137566 185 G 0.9864628911 186 M 0.9874262810 187 K 0.9877581596 188 P 0.9874918461 189 H 0.9865961075 190 Q 0.9852035046 191 K 0.9831839204 192 K 0.9805755615 193 S 0.9774577022 194 S 0.9726486802 195 K 0.9664878249 196 P 0.9587950110 197 N 0.9507102966 198 G 0.9420812130 199 H 0.9335042834 200 S 0.9251780510 201 R 0.9166570306 202 R 0.9078938961 203 G 0.8988140225 204 N 0.8898977637 205 L 0.8810328841 206 S 0.8722431064 207 G 0.8640000820 208 E 0.8563218117 209 V 0.8492720723 210 G 0.8425521255 211 G 0.8361129761 212 S 0.8298907876 213 S 0.8232998252 214 S 0.8165758252 215 S 0.8096819520 216 L 0.8029594421 217 P 0.7961145043 218 S 0.7891162038 219 G 0.7817986608 220 A 0.7739841938 221 Q 0.7666627765

222 T 0.7600855827 223 G 0.7545640469 224 E 0.7492833734 225 W 0.7463501096 226 I 0.7455788255 227 D 0.7463886142 228 N 0.7467793822 229 D 0.7468461990 230 Y 0.7469899058 231 G 0.7469524741 232 D 0.7474706173 233 G 0.7483258247 234 S 0.7497397065 235 S 0.7512066960 236 E 0.7530043721 237 Y 0.7550315857 238 S 0.7565171123 239 G 0.7574564815 240 V 0.7574818134 241 S 0.7567691207 242 T 0.7561950684

Appendix 5

Result of order and disorder predictions in the entire sequence for 97 protein samples

D in O in section section section section section sequence sequence Sequence cdsprotid Length of Length of Length of D in entire D in entire O in entire nonoverlap nonoverlap nonoverlap Protein Pair Pair Protein D in overlap D in O in overlap overlap O in overlap section overlap entire sequence entire sequence 1 1 NP_X10000 131 41 90 133 92 41 2 2 0 2 1 NP_047196 131 109 22 972 201 771 841 179 662 3 2 NP_690837 134 23 111 149 126 23 15 15 0 4 2 NP_690838 134 119 15 1012 111 901 878 96 782 5 3 NP_694621 119 46 73 418 131 287 299 58 241 6 3 NP_694622 119 94 25 119 25 94 0 0 0 7 4 NP_620541 326 326 0 1255 0 1255 929 0 929 8 4 NP_620542 326 161 165 326 165 161 0 0 0 9 5 NP_620538 92 41 51 312 123 189 220 72 148 10 5 NP_X10001 92 20 72 92 72 20 0 0 0 11 6 NP_042580 49 18 31 697 42 655 648 11 637 12 6 NP_042581 49 22 27 863 44 819 814 17 797 13 7 NP_047213 101 101 0 233 0 233 132 0 132 14 7 NP_047214 101 80 21 101 21 80 0 0 0 15 8 NP_056919 186 54 132 507 366 141 321 234 87 16 8 NP_056920 186 91 95 186 95 91 0 0 0 17 9 NP_054691 53 52 1 527 462 65 474 461 13 18 9 NP_054692 53 36 17 282 233 49 229 216 13 19 10 NP_054693 153 65 88 153 88 65 0 0 0 20 11 NP_958049 52 0 52 542 408 134 490 356 134 21 11 NP_958050 52 33 19 295 198 97 243 179 64 22 12 NP_958051 152 74 78 152 78 74 0 0 0 23 13 NP_056872 208 97 111 215 114 101 7 3 4 24 13 NP_056873 208 0 208 568 499 69 360 291 69 25 14 NP_X10005 52 12 40 489 362 127 437 322 115 26 15 NP_054708 69 10 59 391 206 185 322 147 175 27 15 NP_054709 224 104 120 224 120 104 0 0 0 28 16 NP_041713 67 0 67 265 131 134 198 64 134 29 16 NP_X10006 67 38 29 67 29 38 0 0 0 30 17 NP_065672 44 44 0 227 0 227 183 0 183 31 17 NP_065673 44 44 0 191 0 191 147 0 147 32 18 NP_065674 36 36 0 175 0 175 139 0 139 33 19 NP_042508 177 177 0 179 0 179 2 0 2 34 19 NP_042509 177 177 0 657 184 473 480 184 296 35 20 NP_042510 53 53 0 420 9 411 367 9 358 36 21 NP_049324 80 25 55 857 154 703 777 99 678 37 21 NP_619631 80 0 80 110 110 0 30 30 0

D in O in section section section section section sequence sequence Sequence cdsprotid Length of Length of Length of D in entire D in entire O in entire nonoverlap nonoverlap nonoverlap Protein Pair Pair Protein D in overlap D in O in overlap overlap O in overlap section overlap entire sequence entire sequence 38 22 NP_620678 102 27 75 797 130 667 695 55 640 39 22 NP_620679 102 99 3 195 3 192 93 0 93 40 23 NP_044335 320 270 50 2105 269 1836 1785 219 1566 41 23 NP_044336 320 230 90 320 90 230 0 0 0 42 24 NP_612812 45 4 41 312 144 168 267 103 164 43 24 NP_612813 45 45 0 147 0 147 102 0 102 44 25 NP_203557 100 86 14 325 136 189 225 122 103 45 25 NP_203558 100 5 95 222 157 65 122 62 60 46 26 NP_042582 127 20 107 1365 178 1187 1238 71 1167 47 26 NP_042583 127 30 97 127 97 30 0 0 0 48 27 NP_042697 50 50 0 112 0 112 62 0 62 49 27 NP_042698 50 28 22 97 22 75 47 0 47 50 28 NP_040552 105 0 105 460 199 261 355 94 261 51 28 NP_040553 105 105 0 193 6 187 88 6 82 52 29 NP_056788 110 0 110 660 212 448 550 102 448 53 29 NP_056787 110 27 83 123 96 27 13 13 0 54 30 NP_604488 63 30 33 131 33 98 68 0 68 55 30 NP_604489 63 20 43 155 43 112 92 0 92 56 31 NP_835266 52 52 0 122 0 122 70 0 70 57 31 NP_835267 52 36 16 155 16 139 103 0 103 58 32 NP_620439 48 48 0 119 0 119 71 0 71 59 32 NP_620440 48 23 25 190 25 165 142 0 142 60 33 NP_066392 63 21 42 962 253 709 899 211 688 61 33 NP_066394 63 0 63 268 77 191 205 14 191 62 34 NP_733849 232 0 232 237 237 0 5 5 0 63 34 NP_733850 232 168 64 244 76 168 12 12 0 64 35 NP_689444 99 0 99 998 148 850 899 49 850 65 35 NP_689446 99 54 45 106 52 54 7 7 0 66 36 NP_599247 75 0 75 983 187 796 908 112 796 67 36 NP_599248 75 47 28 75 28 47 0 0 0 68 37 NP_919036 133 0 133 1045 163 882 912 30 882 69 37 NP_919037 133 87 46 133 46 87 0 0 0 70 38 NP_040350 156 61 95 2303 138 2165 2147 43 2104 71 38 NP_X10009 156 156 0 156 0 156 0 0 0 72 39 NP_048059 610 64 546 1925 617 1308 1315 71 1244 73 39 NP_048060 610 500 110 612 112 500 2 2 0 74 40 YP_025095 127 13 114 158 114 44 31 0 31 75 40 YP_025096 127 66 61 643 97 546 516 36 480 76 41 NP_051033 130 111 19 242 88 154 112 69 43 77 41 NP_051034 130 86 44 130 44 86 0 0 0 78 42 NP_619671 209 158 51 735 53 682 526 2 524 79 42 NP_X10010 209 156 53 209 53 156 0 0 0

D in O in section section section section section sequence sequence Sequence cdsprotid Length of Length of Length of D in entire D in entire O in entire nonoverlap nonoverlap nonoverlap Protein Pair Pair Protein D in overlap D in O in overlap overlap O in overlap section overlap entire sequence entire sequence 80 43 NP_619676 224 202 22 345 22 323 121 0 121 81 43 NP_619677 224 224 0 224 0 224 0 0 0 82 44 NP_619720 150 84 66 279 117 162 129 51 78 83 44 NP_619722 150 95 55 236 55 181 86 0 86 84 45 NP_608313 50 50 0 62 0 62 12 0 12 85 45 NP_608314 50 0 50 65 65 0 15 15 0 86 46 NP_613263 172 133 39 189 56 133 17 17 0 87 46 NP_613264 172 53 119 172 119 53 0 0 0 88 47 NP_663296 626 0 626 628 628 0 2 2 0 89 47 NP_663297 626 477 149 1844 522 1322 1218 373 845 90 48 NP_041734 114 95 19 1834 554 1280 1720 535 1185 91 48 NP_041736 114 89 25 131 25 106 17 0 17 92 49 NP_043862 52 0 52 217 53 164 165 1 164 93 49 NP_X10011 52 27 25 877 226 651 825 201 624 94 50 NP_043865 427 278 149 427 149 278 0 0 0 95 51 NP_043868 85 19 66 138 66 72 53 0 53 96 52 NP_861408 31 0 31 178 133 45 147 102 45 97 52 NP_861409 31 3 28 501 243 258 470 215 255 Total 13639 7235 6404 43304 12471 30833 29665 6067 23598

Appendix 6 Curriculum Vitae

Mahvash Khosravi 200 Scranton Ct. Zionsville, IN 46077 (317) 873-5923 [email protected]

Education:

2007 Master of Science in Bioinformatics. Indiana University Purdue University, Indianapolis, IN

1991 Ph.D. degree in Molecular Biology Illinois Institute of Technology, Department of Biology, Chicago, IL Thesis title : "Use of Genetic Engineering to Optimize Protein Production in Recombinant Escherichia coli"

1987 M.S. in Molecular Biology Illinois Institute of Technology, Department of Biology, Chicago, IL Thesis title: "Effect of Plasmid Size on Growth and Physiology of Recombinant Escherichia coli"

1977 Jondi-Shapour University, Iran B.S. in Biology

Experience:

Teaching and undergraduate research at the Department of Chemistry and Life Sciences, Rose-Hulman Institute of Technology, Terre Haute, Indiana. Teaching areas have been molecular biology and genetics.

Research and clinical experience as visiting research faculty and postdoctoral fellow at Indiana University Medical Center. Research areas have been neurodegenerative diseases, hereditary diseases, eukaryotic genetics and study and characterization of novel cerebellar cDNA clones.